MultiQC Report

General Statistics

Showing ⁰/₉₀ rows and ²⁰/₃₅ columns.

Sample Name	dupInt	Duplication	5'-3' bias	M Aligned	Proper Pairs	Error rate	Non-primary	Reads mapped	% Mapped	% Proper pairs	% MapQ 0 reads	Total seqs	Reads	Reads mapped	% Reads mapped	rRNA	Total reads	Aligned	Aligned	Uniq aligned	Uniq aligned	Multimapped	Dups	GC	Avg len	Median len	Failed	Seqs	Trimmed bases	Dups	GC	Avg len	Median len	Failed	Seqs
Control_SG37BCR	0.1	29.7%	1.39	29.5M	64.3%	0.63%	3.4M	59.1M	100.0%	99.8%	0.2%	59.1M	62.5M	62.5M	100.0%		32.1M	29.6M	92.3%	28.5M	88.7%	1.2M
Control_SG37BCR_1																							65.2%	48.0%	150bp	150bp	27%	33.9M	5.0%	65.1%	48.0%	143bp	147bp	18%	33.7M
Control_SG37BCR_2																4.2%							64.7%	49.0%	150bp	150bp	27%	33.9M	4.6%	64.6%	48.0%	143bp	147bp	27%	33.7M
Control_SG40ACR	0.2	33.0%	1.36	29.8M	61.0%	0.72%	3.7M	59.7M	100.0%	99.8%	0.1%	59.7M	63.4M	63.4M	100.0%		36.3M	29.9M	82.3%	28.5M	78.6%	1.4M
Control_SG40ACR_1																							64.7%	47.0%	150bp	150bp	36%	43.9M	4.9%	64.7%	47.0%	143bp	150bp	27%	43.9M
Control_SG40ACR_2																16.6%							64.8%	48.0%	150bp	150bp	36%	43.9M	4.9%	64.8%	48.0%	142bp	147bp	36%	43.9M
Control_SG68ACR	0.2	34.2%	1.37	41.7M	60.0%	0.63%	5.0M	83.6M	100.0%	99.8%	0.2%	83.6M	88.6M	88.6M	100.0%		48.6M	41.9M	86.1%	40.2M	82.6%	1.7M
Control_SG68ACR_1																							66.5%	48.0%	150bp	150bp	36%	55.1M	5.1%	66.5%	47.0%	142bp	150bp	27%	55.1M
Control_SG68ACR_2																10.9%							66.6%	48.0%	150bp	150bp	27%	55.1M	5.1%	66.6%	48.0%	142bp	147bp	36%	55.1M
Control_SG6BCR	0.1	30.7%	1.16	19.8M	61.8%	0.65%	3.1M	39.7M	100.0%	99.8%	0.3%	39.7M	42.9M	42.9M	100.0%		34.0M	19.9M	58.5%	19.0M	55.7%	0.9M
Control_SG6BCR_1																							67.6%	45.0%	150bp	150bp	45%	41.0M	5.3%	67.6%	44.0%	142bp	150bp	36%	40.9M
Control_SG6BCR_2																15.8%							68.3%	45.0%	150bp	150bp	45%	41.0M	5.2%	68.3%	45.0%	142bp	147bp	36%	40.9M
Control_SG7ACR	0.1	34.2%	1.02	33.6M	58.4%	0.63%	5.5M	67.3M	100.0%	99.8%	0.3%	67.3M	72.8M	72.8M	100.0%		41.4M	33.7M	81.5%	32.0M	77.4%	1.7M
Control_SG7ACR_1																							70.9%	47.0%	150bp	150bp	36%	48.4M	6.7%	70.9%	47.0%	140bp	147bp	27%	48.4M
Control_SG7ACR_2																13.9%							71.3%	48.0%	150bp	150bp	36%	48.4M	6.5%	71.3%	48.0%	140bp	147bp	36%	48.4M
Control_SG86ACR	0.2	30.7%	1.41	29.9M	63.8%	0.64%	3.1M	59.9M	100.0%	99.8%	0.1%	59.9M	63.1M	63.1M	100.0%		34.6M	30.0M	86.8%	28.9M	83.7%	1.1M
Control_SG86ACR_1																							63.2%	48.0%	150bp	150bp	27%	37.7M	4.2%	63.3%	48.0%	144bp	150bp	18%	37.7M
Control_SG86ACR_2																7.5%							63.0%	48.0%	150bp	150bp	27%	37.7M	4.2%	63.0%	48.0%	144bp	147bp	27%	37.7M
Park_SG109BCR	0.1	34.2%	1.40	42.3M	59.8%	0.66%	5.4M	84.8M	100.0%	99.8%	0.1%	84.8M	90.2M	90.2M	100.0%		47.0M	42.5M	90.5%	40.4M	86.1%	2.1M
Park_SG109BCR_1																							67.2%	47.0%	150bp	150bp	27%	50.9M	4.4%	67.2%	47.0%	143bp	150bp	18%	50.9M
Park_SG109BCR_2																7.0%							67.1%	48.0%	150bp	150bp	27%	50.9M	4.3%	67.1%	48.0%	143bp	147bp	27%	50.9M
Park_SG17BCR	0.2	33.0%	1.36	34.8M	61.6%	0.63%	3.7M	69.8M	100.0%	99.8%	0.1%	69.8M	73.5M	73.5M	100.0%		39.5M	35.0M	88.4%	33.6M	85.1%	1.3M
Park_SG17BCR_1																							65.6%	48.0%	150bp	150bp	18%	42.3M	3.0%	65.6%	48.0%	145bp	150bp	18%	42.3M
Park_SG17BCR_2																5.7%							66.0%	49.0%	150bp	150bp	18%	42.3M	3.0%	66.0%	49.0%	145bp	150bp	27%	42.3M
Park_SG18ACR	0.1	31.4%	1.40	42.5M	63.2%	0.70%	4.5M	85.1M	100.0%	99.9%	0.1%	85.1M	89.6M	89.6M	100.0%		46.6M	42.6M	91.5%	41.0M	88.0%	1.6M
Park_SG18ACR_1																							62.5%	47.0%	150bp	150bp	27%	48.2M	4.3%	62.6%	47.0%	143bp	150bp	18%	48.1M
Park_SG18ACR_2																2.6%							61.5%	48.0%	150bp	150bp	27%	48.2M	4.4%	61.5%	48.0%	143bp	147bp	27%	48.1M
Park_SG23ACR	0.1	31.3%	1.29	38.4M	62.9%	0.62%	4.4M	76.8M	100.0%	99.8%	0.2%	76.8M	81.2M	81.2M	100.0%		41.5M	38.5M	92.7%	37.0M	89.1%	1.5M
Park_SG23ACR_1																							66.4%	48.0%	150bp	150bp	27%	43.7M	4.0%	66.4%	48.0%	144bp	150bp	18%	43.6M
Park_SG23ACR_2																4.1%							66.4%	49.0%	150bp	150bp	27%	43.7M	4.0%	66.4%	48.0%	144bp	147bp	27%	43.6M
Park_SG48BCR	0.2	36.7%	1.23	26.8M	56.9%	0.63%	3.8M	53.7M	100.0%	99.8%	0.2%	53.7M	57.5M	57.5M	100.0%		37.2M	26.9M	72.4%	25.7M	69.0%	1.3M
Park_SG48BCR_1																							64.2%	46.0%	150bp	150bp	27%	42.1M	5.2%	64.2%	46.0%	142bp	147bp	18%	42.1M
Park_SG48BCR_2																10.7%							64.3%	47.0%	150bp	150bp	27%	42.1M	5.0%	64.3%	47.0%	142bp	147bp	27%	42.1M
Park_SG53ACR	0.2	31.2%	1.31	21.9M	62.0%	0.61%	3.0M	43.9M	100.0%	99.8%	0.2%	43.9M	46.9M	46.9M	100.0%		35.6M	22.0M	61.8%	21.0M	59.1%	1.0M
Park_SG53ACR_1																							60.4%	47.0%	150bp	150bp	27%	45.5M	5.5%	60.4%	46.0%	142bp	150bp	18%	45.4M
Park_SG53ACR_2																20.8%							62.0%	47.0%	150bp	150bp	36%	45.5M	5.5%	62.0%	47.0%	142bp	147bp	27%	45.4M
Park_SG73BCR	0.1	42.9%	1.24	65.7M	50.8%	0.63%	11.0M	131.7M	100.0%	99.8%	0.3%	131.7M	142.7M	142.7M	100.0%		79.8M	66.0M	82.7%	62.5M	78.3%	3.5M
Park_SG73BCR_1																							77.6%	47.0%	150bp	150bp	36%	86.8M	7.4%	77.7%	46.0%	139bp	147bp	27%	86.8M
Park_SG73BCR_2																7.2%							76.6%	47.0%	150bp	150bp	36%	86.8M	7.1%	76.6%	47.0%	139bp	147bp	36%	86.8M
Park_SG90ACR	0.2	30.1%	1.28	12.7M	62.2%	0.71%	2.0M	25.4M	100.0%	99.8%	0.3%	25.4M	27.5M	27.5M	100.0%		25.6M	12.7M	49.7%	12.1M	47.2%	0.6M
Park_SG90ACR_1																							61.9%	47.0%	150bp	150bp	45%	40.4M	5.5%	61.9%	47.0%	142bp	150bp	36%	40.4M
Park_SG90ACR_2																35.3%							65.1%	48.0%	150bp	150bp	36%	40.4M	5.5%	65.1%	47.0%	141bp	147bp	36%	40.4M
Park_SG94BCR	0.1	32.4%	1.32	43.7M	61.9%	0.63%	4.9M	87.6M	100.0%	99.8%	0.2%	87.6M	92.6M	92.6M	100.0%		47.1M	43.9M	93.2%	42.2M	89.6%	1.7M
Park_SG94BCR_1																							67.1%	48.0%	150bp	150bp	27%	48.7M	4.5%	67.1%	48.0%	143bp	150bp	18%	48.7M
Park_SG94BCR_2																2.6%							67.0%	49.0%	150bp	150bp	27%	48.7M	4.6%	67.0%	49.0%	143bp	147bp	27%	48.7M
Park_SG95ACR	0.2	32.6%	1.32	31.8M	61.7%	0.62%	3.6M	63.7M	100.0%	99.8%	0.2%	63.7M	67.4M	67.4M	100.0%		35.2M	31.9M	90.7%	30.7M	87.1%	1.2M
Park_SG95ACR_1																							64.9%	48.0%	150bp	150bp	27%	37.7M	5.8%	64.9%	48.0%	141bp	147bp	18%	37.7M
Park_SG95ACR_2																5.8%							64.4%	49.0%	150bp	150bp	27%	37.7M	5.7%	64.4%	49.0%	141bp	147bp	27%	37.7M
Urban_SG30ACR	0.1	32.6%	1.39	30.1M	61.0%	0.63%	3.9M	60.3M	100.0%	99.8%	0.2%	60.3M	64.2M	64.2M	100.0%		40.2M	30.2M	75.2%	28.9M	72.0%	1.3M
Urban_SG30ACR_1																							68.9%	46.0%	150bp	150bp	27%	43.4M	4.8%	68.9%	46.0%	143bp	150bp	18%	43.4M
Urban_SG30ACR_2																6.6%							69.1%	47.0%	150bp	150bp	36%	43.4M	4.7%	69.1%	47.0%	143bp	147bp	36%	43.4M
Urban_SG32BCR	0.1	33.3%	1.26	30.8M	60.1%	0.66%	4.2M	61.6M	100.0%	99.8%	0.2%	61.6M	65.9M	65.9M	100.0%		34.4M	30.9M	89.7%	29.5M	85.7%	1.4M
Urban_SG32BCR_1																							67.2%	48.0%	150bp	150bp	27%	36.9M	5.8%	67.2%	48.0%	141bp	147bp	18%	36.8M
Urban_SG32BCR_2																5.5%							67.3%	49.0%	150bp	150bp	27%	36.9M	5.6%	67.3%	49.0%	141bp	147bp	27%	36.8M
Urban_SG33BCR	0.1	30.5%	1.38	32.8M	63.7%	0.64%	3.7M	65.7M	100.0%	99.8%	0.2%	65.7M	69.4M	69.4M	100.0%		36.5M	32.9M	90.1%	31.7M	86.7%	1.2M
Urban_SG33BCR_1																							64.3%	48.0%	150bp	150bp	27%	39.0M	4.0%	64.3%	48.0%	144bp	150bp	18%	39.0M
Urban_SG33BCR_2																5.5%							63.9%	48.0%	150bp	150bp	27%	39.0M	3.9%	64.0%	48.0%	144bp	150bp	27%	39.0M
Urban_SG34BCR	0.1	34.2%	1.33	49.5M	59.9%	0.67%	6.2M	99.1M	100.0%	99.8%	0.2%	99.1M	105.3M	105.3M	100.0%		54.5M	49.6M	91.1%	47.5M	87.2%	2.1M
Urban_SG34BCR_1																							70.4%	48.0%	150bp	150bp	27%	56.7M	5.8%	70.4%	48.0%	141bp	147bp	18%	56.7M
Urban_SG34BCR_2																3.3%							70.0%	49.0%	150bp	150bp	27%	56.7M	5.6%	70.0%	48.0%	141bp	147bp	27%	56.7M
Urban_SG55BCR	0.2	34.3%	1.30	35.4M	59.5%	0.74%	4.8M	71.0M	100.0%	99.8%	0.2%	71.0M	75.8M	75.8M	100.0%		44.0M	35.5M	80.7%	33.8M	76.9%	1.7M
Urban_SG55BCR_1																							66.8%	46.0%	150bp	150bp	27%	49.8M	4.7%	66.8%	46.0%	143bp	150bp	18%	49.8M
Urban_SG55BCR_2																10.8%							66.0%	47.0%	150bp	150bp	27%	49.8M	4.5%	66.0%	46.0%	143bp	147bp	27%	49.8M
Urban_SG56BCR	0.1	32.7%	1.35	34.2M	61.0%	0.60%	4.4M	68.5M	100.0%	99.8%	0.2%	68.5M	72.9M	72.9M	100.0%		39.0M	34.3M	88.1%	32.9M	84.3%	1.5M
Urban_SG56BCR_1																							67.5%	48.0%	150bp	150bp	27%	42.4M	4.8%	67.5%	48.0%	143bp	150bp	18%	42.4M
Urban_SG56BCR_2																7.4%							67.9%	48.0%	150bp	150bp	27%	42.4M	4.7%	67.9%	48.0%	143bp	147bp	27%	42.4M
Urban_SG62ACR	0.1	34.3%	1.32	40.1M	59.4%	0.62%	5.4M	80.5M	100.0%	99.8%	0.2%	80.5M	85.8M	85.8M	100.0%		49.8M	40.3M	80.9%	38.6M	77.5%	1.7M
Urban_SG62ACR_1																							68.5%	48.0%	150bp	150bp	27%	55.4M	5.2%	68.5%	48.0%	142bp	150bp	27%	55.4M
Urban_SG62ACR_2																9.1%							68.7%	49.0%	150bp	150bp	27%	55.4M	5.0%	68.7%	48.0%	142bp	147bp	27%	55.4M
Urban_SG64BCR	0.1	31.7%	1.17	34.3M	61.0%	0.61%	5.3M	68.7M	100.0%	99.8%	0.3%	68.7M	74.0M	74.0M	100.0%		40.3M	34.4M	85.3%	32.8M	81.3%	1.6M
Urban_SG64BCR_1																							65.5%	49.0%	150bp	150bp	27%	45.6M	8.6%	65.6%	48.0%	137bp	147bp	18%	45.6M
Urban_SG64BCR_2																10.6%							66.8%	49.0%	150bp	150bp	27%	45.6M	8.5%	66.8%	48.0%	137bp	147bp	36%	45.6M
Urban_SG78ACR	0.2	30.5%	1.13	14.7M	61.4%	0.61%	2.5M	29.4M	100.0%	99.8%	0.3%	29.4M	31.9M	31.9M	100.0%		38.8M	14.7M	38.0%	14.0M	36.1%	0.7M
Urban_SG78ACR_1																							82.6%	43.0%	150bp	150bp	36%	47.3M	6.9%	82.7%	43.0%	140bp	147bp	27%	47.2M
Urban_SG78ACR_2																17.4%							82.7%	44.0%	150bp	150bp	55%	47.3M	6.9%	82.7%	43.0%	139bp	147bp	45%	47.2M
Urban_SG79ACR	0.1	32.0%	1.30	31.4M	61.2%	0.64%	4.4M	62.8M	100.0%	99.9%	0.2%	62.8M	67.2M	67.2M	100.0%		33.9M	31.4M	92.9%	30.0M	88.6%	1.5M
Urban_SG79ACR_1																							69.2%	48.0%	150bp	150bp	27%	35.1M	4.2%	69.2%	47.0%	144bp	150bp	18%	35.1M
Urban_SG79ACR_2																2.8%							68.7%	48.0%	150bp	150bp	27%	35.1M	4.1%	68.7%	48.0%	144bp	147bp	27%	35.1M
Urban_SG80BCR	0.1	34.4%	1.30	33.0M	59.5%	0.71%	4.3M	66.1M	100.0%	99.8%	0.2%	66.1M	70.4M	70.4M	100.0%		36.5M	33.1M	90.6%	31.5M	86.4%	1.5M
Urban_SG80BCR_1																							64.8%	47.0%	150bp	150bp	27%	38.3M	4.5%	64.8%	46.0%	143bp	150bp	18%	38.2M
Urban_SG80BCR_2																3.8%							64.4%	47.0%	150bp	150bp	27%	38.3M	4.2%	64.4%	47.0%	143bp	147bp	27%	38.2M
Urban_SG82ACR	0.1	31.6%	1.32	18.6M	61.7%	0.64%	2.5M	37.3M	100.0%	99.7%	0.2%	37.3M	39.8M	39.8M	100.0%		27.1M	18.7M	69.0%	17.9M	65.9%	0.8M
Urban_SG82ACR_1																							58.8%	46.0%	150bp	150bp	36%	31.9M	5.9%	58.8%	45.0%	141bp	147bp	27%	31.8M
Urban_SG82ACR_2																13.4%							58.2%	46.0%	150bp	150bp	36%	31.9M	5.8%	58.2%	46.0%	141bp	147bp	36%	31.8M
Urban_SG84ACR	0.2	32.9%	1.39	37.6M	61.1%	0.61%	4.6M	75.4M	100.0%	99.8%	0.2%	75.4M	80.0M	80.0M	100.0%		47.5M	37.8M	79.5%	36.2M	76.2%	1.5M
Urban_SG84ACR_1																							61.6%	47.0%	150bp	150bp	27%	53.2M	5.8%	61.6%	47.0%	141bp	147bp	27%	53.2M
Urban_SG84ACR_2																9.7%							62.3%	48.0%	150bp	150bp	36%	53.2M	5.9%	62.3%	48.0%	141bp	147bp	36%	53.2M
Urban_SG97BCR	0.1	33.3%	1.30	39.3M	60.3%	0.63%	5.3M	78.7M	100.0%	99.8%	0.2%	78.7M	83.9M	83.9M	100.0%		44.7M	39.4M	88.2%	37.7M	84.4%	1.7M
Urban_SG97BCR_1																							69.7%	48.0%	150bp	150bp	27%	47.7M	6.1%	69.8%	48.0%	141bp	147bp	18%	47.7M
Urban_SG97BCR_2																5.4%							70.2%	49.0%	150bp	150bp	27%	47.7M	5.9%	70.2%	48.0%	141bp	147bp	27%	47.7M

Uncheck the tick box to hide columns. Click and drag the handle on the left to change order. Table ID: general_stats_table_table

Sort	Group	Column	Description	ID	Scale
\|\|	Custom content: DupRadar	dupInt	Intercept value from DupRadar	`custom_content_dupradar-dupRadar_intercept`
\|\|	Picard: Mark Duplicates	Duplication	Mark Duplicates - Percent Duplication	`picard_mark_duplicates-PERCENT_DUPLICATION`
\|\|	QualiMap: RNASeq	5'-3' bias	5'-3' bias	`qualimap_rnaseq-5_3_bias`
\|\|	QualiMap: RNASeq	M Aligned	Reads Aligned (millions)	`qualimap_rnaseq-reads_aligned`	read_count
\|\|	RSeQC: Bam Stat	Proper Pairs	% Reads mapped in proper pairs	`rseqc_bam_stat-proper_pairs_percent`
\|\|	Samtools: stats	Error rate	Error rate: mismatches (NM) / bases mapped (CIGAR)	`samtools_stats-error_rate`
\|\|	Samtools: stats	Non-primary	Non-primary alignments (millions)	`samtools_stats-non_primary_alignments`	read_count
\|\|	Samtools: stats	Reads mapped	Reads mapped in the bam file (millions)	`samtools_stats-reads_mapped`	read_count
\|\|	Samtools: stats	% Mapped	% Mapped reads	`samtools_stats-reads_mapped_percent`
\|\|	Samtools: stats	% Proper pairs	% Properly paired reads	`samtools_stats-reads_properly_paired_percent`
\|\|	Samtools: stats	% MapQ 0 reads	% of reads that are ambiguously placed (MapQ=0)	`samtools_stats-reads_MQ0_percent`
\|\|	Samtools: stats	Total seqs	Total sequences in the bam file (millions)	`samtools_stats-raw_total_sequences`	read_count
\|\|	Samtools: flagstat	Reads	Total reads in the bam file (millions)	`samtools_flagstat-flagstat_total`	read_count
\|\|	Samtools: flagstat	Reads mapped	Reads mapped in the bam file (millions)	`samtools_flagstat-mapped_passed`	read_count
\|\|	Samtools: flagstat	% Reads mapped	% Reads mapped in the bam file	`samtools_flagstat-mapped_passed_pct`
\|\|	SortMeRNA	rRNA	Percentage of reads matched to a SortMeRNA database	`sortmerna-rRNA_pct`
\|\|	STAR	Total reads	Number of input reads	`star-total_reads`	read_count
\|\|	STAR	Aligned	Mapped reads	`star-mapped`	read_count
\|\|	STAR	Aligned	% Mapped reads	`star-mapped_percent`
\|\|	STAR	Uniq aligned	Uniquely mapped reads	`star-uniquely_mapped`	read_count
\|\|	STAR	Uniq aligned	% Uniquely mapped reads	`star-uniquely_mapped_percent`
\|\|	STAR	Multimapped	Multiple mapped reads	`star-multimapped`	read_count
\|\|	FastQC (raw)	Dups	% duplicate reads	`fastqc_raw-percent_duplicates`
\|\|	FastQC (raw)	GC	Average % GC content	`fastqc_raw-percent_gc`
\|\|	FastQC (raw)	Avg len	Average read length	`fastqc_raw-avg_sequence_length`
\|\|	FastQC (raw)	Median len	Median read length	`fastqc_raw-median_sequence_length`
\|\|	FastQC (raw)	Failed	Percentage of modules failed in FastQC report (includes those not plotted here)	`fastqc_raw-percent_fails`
\|\|	FastQC (raw)	Seqs	Total sequences (millions)	`fastqc_raw-total_sequences`
\|\|	Cutadapt	Trimmed bases	% total base pairs trimmed	`cutadapt-percent_trimmed`
\|\|	FastQC (trimmed)	Dups	% duplicate reads	`fastqc_trimmed-percent_duplicates`
\|\|	FastQC (trimmed)	GC	Average % GC content	`fastqc_trimmed-percent_gc`
\|\|	FastQC (trimmed)	Avg len	Average read length	`fastqc_trimmed-avg_sequence_length`
\|\|	FastQC (trimmed)	Median len	Median read length	`fastqc_trimmed-median_sequence_length`
\|\|	FastQC (trimmed)	Failed	Percentage of modules failed in FastQC report (includes those not plotted here)	`fastqc_trimmed-percent_fails`
\|\|	FastQC (trimmed)	Seqs	Total sequences (millions)	`fastqc_trimmed-total_sequences`

Sample status checks

Reports on sample strandedness status, and any failures in trimming or mapping.

Strandedness Checks

Showing ⁰/₆₀ rows and ⁷/₇ columns.

Sample	Status	Strand inference method	Provided strandedness	Inferred strandedness	Sense (%)	Antisense (%)	Unstranded (%)
Control_SG37BCR	pass	RSeQC	auto	reverse	2.4	94.2	3.4
Control_SG37BCR	pass	Salmon	auto	reverse	1.2	98.8	0.0
Control_SG40ACR	pass	RSeQC	auto	reverse	2.5	94.0	3.4
Control_SG40ACR	pass	Salmon	auto	reverse	1.5	98.5	0.0
Control_SG68ACR	pass	RSeQC	auto	reverse	2.7	93.7	3.7
Control_SG68ACR	pass	Salmon	auto	reverse	1.4	98.6	0.0
Control_SG6BCR	pass	RSeQC	auto	reverse	3.2	94.3	2.5
Control_SG6BCR	pass	Salmon	auto	reverse	1.5	98.5	0.0
Control_SG7ACR	pass	RSeQC	auto	reverse	2.6	94.0	3.5
Control_SG7ACR	pass	Salmon	auto	reverse	1.4	98.6	0.0
Control_SG86ACR	pass	RSeQC	auto	reverse	2.2	94.5	3.3
Control_SG86ACR	pass	Salmon	auto	reverse	1.4	98.6	0.0
Park_SG109BCR	pass	RSeQC	auto	reverse	2.7	93.1	4.2
Park_SG109BCR	pass	Salmon	auto	reverse	1.4	98.6	0.0
Park_SG17BCR	pass	RSeQC	auto	reverse	0.4	96.4	3.3
Park_SG17BCR	pass	Salmon	auto	reverse	1.5	98.5	0.0
Park_SG18ACR	pass	RSeQC	auto	reverse	0.6	95.0	4.4
Park_SG18ACR	pass	Salmon	auto	reverse	1.4	98.6	0.0
Park_SG23ACR	pass	RSeQC	auto	reverse	0.4	95.3	4.2
Park_SG23ACR	pass	Salmon	auto	reverse	1.2	98.8	0.0
Park_SG48BCR	pass	RSeQC	auto	reverse	2.5	94.3	3.2
Park_SG48BCR	pass	Salmon	auto	reverse	1.7	98.3	0.0
Park_SG53ACR	pass	RSeQC	auto	reverse	3.2	93.7	3.0
Park_SG53ACR	pass	Salmon	auto	reverse	1.3	98.7	0.0
Park_SG73BCR	pass	RSeQC	auto	reverse	0.8	94.5	4.7
Park_SG73BCR	pass	Salmon	auto	reverse	1.7	98.3	0.0
Park_SG90ACR	pass	RSeQC	auto	reverse	2.3	95.5	2.2
Park_SG90ACR	pass	Salmon	auto	reverse	1.6	98.4	0.0
Park_SG94BCR	pass	RSeQC	auto	reverse	0.6	95.0	4.3
Park_SG94BCR	pass	Salmon	auto	reverse	1.3	98.7	0.0
Park_SG95ACR	pass	RSeQC	auto	reverse	2.5	94.3	3.2
Park_SG95ACR	pass	Salmon	auto	reverse	1.3	98.7	0.0
Urban_SG30ACR	pass	RSeQC	auto	reverse	2.2	94.7	3.2
Urban_SG30ACR	pass	Salmon	auto	reverse	1.4	98.6	0.0
Urban_SG32BCR	pass	RSeQC	auto	reverse	0.7	96.6	2.7
Urban_SG32BCR	pass	Salmon	auto	reverse	1.8	98.2	0.0
Urban_SG33BCR	pass	RSeQC	auto	reverse	2.4	93.3	4.3
Urban_SG33BCR	pass	Salmon	auto	reverse	1.3	98.7	0.0
Urban_SG34BCR	pass	RSeQC	auto	reverse	0.7	94.2	5.1
Urban_SG34BCR	pass	Salmon	auto	reverse	1.4	98.6	0.0
Urban_SG55BCR	pass	RSeQC	auto	reverse	3.0	92.1	4.9
Urban_SG55BCR	pass	Salmon	auto	reverse	1.5	98.5	0.0
Urban_SG56BCR	pass	RSeQC	auto	reverse	0.5	95.9	3.6
Urban_SG56BCR	pass	Salmon	auto	reverse	1.3	98.7	0.0
Urban_SG62ACR	pass	RSeQC	auto	reverse	0.5	95.4	4.1
Urban_SG62ACR	pass	Salmon	auto	reverse	1.5	98.5	0.0
Urban_SG64BCR	pass	RSeQC	auto	reverse	2.8	93.8	3.4
Urban_SG64BCR	pass	Salmon	auto	reverse	1.3	98.7	0.0
Urban_SG78ACR	pass	RSeQC	auto	reverse	2.4	95.8	1.8
Urban_SG78ACR	pass	Salmon	auto	reverse	1.6	98.4	0.0
Urban_SG79ACR	pass	RSeQC	auto	reverse	2.7	93.1	4.2
Urban_SG79ACR	pass	Salmon	auto	reverse	1.2	98.8	0.0
Urban_SG80BCR	pass	RSeQC	auto	reverse	3.1	93.2	3.7
Urban_SG80BCR	pass	Salmon	auto	reverse	1.5	98.5	0.0
Urban_SG82ACR	pass	RSeQC	auto	reverse	2.8	94.1	3.2
Urban_SG82ACR	pass	Salmon	auto	reverse	1.7	98.3	0.0
Urban_SG84ACR	pass	RSeQC	auto	reverse	0.5	95.7	3.7
Urban_SG84ACR	pass	Salmon	auto	reverse	1.3	98.7	0.0
Urban_SG97BCR	pass	RSeQC	auto	reverse	0.6	95.6	3.8
Urban_SG97BCR	pass	Salmon	auto	reverse	1.5	98.5	0.0

Uncheck the tick box to hide columns. Click and drag the handle on the left to change order. Table ID: fail_strand_check_table_table

Sort	Column	Description	ID
\|\|	Status	Pass if the strandedness inferred by RSeQC matches that provided by the user or predicted by Salmon. Fail otherwise.	`samples_strandedness_check-Status`
\|\|	Strand inference method	Tool used to detect strandedness	`samples_strandedness_check-Strand_inference_method`
\|\|	Provided strandedness	User-provided strandedness	`samples_strandedness_check-Provided_strandedness`
\|\|	Inferred strandedness	Interred strandedness	`samples_strandedness_check-Inferred_strandedness`
\|\|	Sense (%)	Sense (%)	`samples_strandedness_check-Sense`
\|\|	Antisense (%)	Antisense (%)	`samples_strandedness_check-Antisense`
\|\|	Unstranded (%)	Unstranded (%)	`samples_strandedness_check-Unstranded`

FastQC (raw)

This section of the report shows FastQC results before adapter trimming.URL: http://www.bioinformatics.babraham.ac.uk/projects/fastqc

Sequence Counts

Sequence counts for each sample. Duplicate read counts are an estimate only.

This plot show the total number of reads, broken down into unique and duplicate if possible (only more recent versions of FastQC give duplicate info).

You can read more about duplicate calculation in the FastQC documentation. A small part has been copied here for convenience:

Only sequences which first appear in the first 100,000 sequences in each file are analysed. This should be enough to get a good impression for the duplication levels in the whole file. Each sequence is tracked to the end of the file to give a representative count of the overall duplication level.

The duplication detection requires an exact sequence match over the whole length of the sequence. Any reads over 75bp in length are truncated to 50bp for this analysis.

Created with MultiQC

Sequence Quality Histograms

60

0

0

60

0

0

60

0

0

The mean quality value across each base position in the read.

To enable multiple samples to be plotted on the same graph, only the mean quality scores are plotted (unlike the box plots seen in FastQC reports).

Taken from the FastQC help:

The y-axis on the graph shows the quality scores. The higher the score, the better the base call. The background of the graph divides the y axis into very good quality calls (green), calls of reasonable quality (orange), and calls of poor quality (red). The quality of calls on most platforms will degrade as the run progresses, so it is common to see base calls falling into the orange area towards the end of a read.

Created with MultiQC

Per Sequence Quality Scores

60

0

0

60

0

0

60

0

0

The number of reads with average quality scores. Shows if a subset of reads has poor quality.

From the FastQC help:

The per sequence quality score report allows you to see if a subset of your sequences have universally low quality values. It is often the case that a subset of sequences will have universally poor quality, however these should represent only a small percentage of the total sequences.

Created with MultiQC

Per Base Sequence Content

The proportion of each base position for which each of the four normal DNA bases has been called.

To enable multiple samples to be shown in a single plot, the base composition data is shown as a heatmap. The colours represent the balance between the four bases: an even distribution should give an even muddy brown colour. Hover over the plot to see the percentage of the four bases under the cursor.

To see the data as a line plot, as in the original FastQC graph, click on a sample track.

From the FastQC help:

Per Base Sequence Content plots out the proportion of each base position in a file for which each of the four normal DNA bases has been called.

In a random library you would expect that there would be little to no difference between the different bases of a sequence run, so the lines in this plot should run parallel with each other. The relative amount of each base should reflect the overall amount of these bases in your genome, but in any case they should not be hugely imbalanced from each other.

It's worth noting that some types of library will always produce biased sequence composition, normally at the start of the read. Libraries produced by priming using random hexamers (including nearly all RNA-Seq libraries) and those which were fragmented using transposases inherit an intrinsic bias in the positions at which reads start. This bias does not concern an absolute sequence, but instead provides enrichement of a number of different K-mers at the 5' end of the reads. Whilst this is a true technical bias, it isn't something which can be corrected by trimming and in most cases doesn't seem to adversely affect the downstream analysis.

Click a sample row to see a line plot for that dataset.

Rollover for sample name

Position: -

%T: -

%C: -

%A: -

%G: -

0

27

33

0

27

33

0

27

33

Per Sequence GC Content

7

36

17

7

36

17

7

36

17

The average GC content of reads. Normal random library typically have a roughly normal distribution of GC content.

From the FastQC help:

This module measures the GC content across the whole length of each sequence in a file and compares it to a modelled normal distribution of GC content.

In a normal random library you would expect to see a roughly normal distribution of GC content where the central peak corresponds to the overall GC content of the underlying genome. Since we don't know the GC content of the genome the modal GC content is calculated from the observed data and used to build a reference distribution.

An unusually shaped distribution could indicate a contaminated library or some other kinds of biased subset. A normal distribution which is shifted indicates some systematic bias which is independent of base position. If there is a systematic bias which creates a shifted normal distribution then this won't be flagged as an error by the module since it doesn't know what your genome's GC content should be.

Created with MultiQC

Per Base N Content

60

0

0

60

0

0

60

0

0

The percentage of base calls at each position for which an N was called.

From the FastQC help:

If a sequencer is unable to make a base call with sufficient confidence then it will normally substitute an N rather than a conventional base call. This graph shows the percentage of base calls at each position for which an N was called.

It's not unusual to see a very low proportion of Ns appearing in a sequence, especially nearer the end of a sequence. However, if this proportion rises above a few percent it suggests that the analysis pipeline was unable to interpret the data well enough to make valid base calls.

Created with MultiQC

Sequence Length Distribution

60

0

0

60

0

0

60

0

0

All samples have sequences of a single length (150bp)

Sequence Duplication Levels

0

0

60

0

0

60

0

0

60

The relative level of duplication found for every sequence.

From the FastQC Help:

In a diverse library most sequences will occur only once in the final set. A low level of duplication may indicate a very high level of coverage of the target sequence, but a high level of duplication is more likely to indicate some kind of enrichment bias (e.g. PCR over amplification). This graph shows the degree of duplication for every sequence in a library: the relative number of sequences with different degrees of duplication.

Only sequences which first appear in the first 100,000 sequences in each file are analysed. This should be enough to get a good impression for the duplication levels in the whole file. Each sequence is tracked to the end of the file to give a representative count of the overall duplication level.

The duplication detection requires an exact sequence match over the whole length of the sequence. Any reads over 75bp in length are truncated to 50bp for this analysis.

In a properly diverse library most sequences should fall into the far left of the plot in both the red and blue lines. A general level of enrichment, indicating broad oversequencing in the library will tend to flatten the lines, lowering the low end and generally raising other categories. More specific enrichments of subsets, or the presence of low complexity contaminants will tend to produce spikes towards the right of the plot.

Created with MultiQC

Overrepresented sequences by sample

0

57

3

0

57

3

0

57

3

The total amount of overrepresented sequences found in each library.

FastQC calculates and lists overrepresented sequences in FastQ files. It would not be possible to show this for all samples in a MultiQC report, so instead this plot shows the number of sequences categorized as overrepresented.

Sometimes, a single sequence may account for a large number of reads in a dataset. To show this, the bars are split into two: the first shows the overrepresented reads that come from the single most common sequence. The second shows the total count from all remaining overrepresented sequences.

From the FastQC Help:

A normal high-throughput library will contain a diverse set of sequences, with no individual sequence making up a tiny fraction of the whole. Finding that a single sequence is very overrepresented in the set either means that it is highly biologically significant, or indicates that the library is contaminated, or not as diverse as you expected.

FastQC lists all the sequences which make up more than 0.1% of the total. To conserve memory only sequences which appear in the first 100,000 sequences are tracked to the end of the file. It is therefore possible that a sequence which is overrepresented but doesn't appear at the start of the file for some reason could be missed by this module.

Created with MultiQC

Top overrepresented sequences

Top overrepresented sequences across all samples. The table shows 20 most overrepresented sequences across all samples, ranked by the number of samples they occur in.

Showing ⁰/₂₀ rows and ³/₃ columns.

Overrepresented sequence	Reports	Occurrences	% of all reads
AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA	30	5223269	0.1908%
GGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGG	30	6604903	0.2412%
CCCCCCTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTT	21	1362778	0.0498%
CTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTT	19	1190599	0.0435%
CCCCCCCTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTT	11	679512	0.0248%
TTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTT	10	593741	0.0217%
CCCGGTTTGGGCTTATCCGGTTTCGCTCGCCGCTACTCCCGGAATCGAGT	8	2304156	0.0842%
GCTGGTTTGGGCTCCTCCGGTTTCGCTCGCCGCTACTCCCGGAATCGAGT	8	802131	0.0293%
CCCCCCCCTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTT	8	482886	0.0176%
CCCCCTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTT	6	384218	0.0140%
CCGGTTTGGGCTTATCCGGTTTCGCTCGCCGCTACTCCCGGAATCGAGTT	3	718979	0.0263%
GGCGGATGCCTTGGCACTGGGAGCCGAAGAAAGACGTGATAAGCTGCGAA	2	139731	0.0051%
GAAAAAAGCAAGGTACAAAAGATTGTTACATAGGATAACATCGAAGGACA	2	102096	0.0037%
CCCGGTTTGGGCTGGCCCCCTTTCGCTCGCCGCTACTCGGGGGTTCGAGT	2	96391	0.0035%
TTTCGGTTTATCCATTCGTATTGGAATTTTTTTGAGAGATATTTTTCTCT	2	532679	0.0195%
CTTTTTTATTGATTGTCTTTATTTGAAACGCGCAATCTTCGTTTGCAACA	2	450770	0.0165%
CAGCCTTTTAAAGAGCCCAGTCTGTCATCGAGACGATCTTTAGCGTTTTG	2	119121	0.0044%
CTCAAGCCTTCTGTAGCTTTTCAGTTCTTTGTTTTTTGGATTCCACTCTT	2	278092	0.0102%
CTCAACTTCTCGAAAGCATAAGAGGTGAACGAATAAACATCAGAAGTTAA	2	769669	0.0281%
GATAAATCCATAAAGAAAAGGGAGGTCATGCAATCATGACAAAAGATGAA	2	181469	0.0066%

Uncheck the tick box to hide columns. Click and drag the handle on the left to change order. Table ID: fastqc_raw_top_overrepresented_sequences_table_table

Sort	Column	Description	ID
\|\|	Reports	Number of FastQC reports where this sequence is founds as overrepresented	`fastqc_raw-samples`
\|\|	Occurrences	Total number of occurrences of the sequence (among the samples where the sequence is overrepresented)	`fastqc_raw-total_count`
\|\|	% of all reads	Total number of occurrences as the percentage of all reads (among samples where the sequence is overrepresented)	`fastqc_raw-total_percent`

Adapter Content

0

2

58

0

2

58

0

2

58

The cumulative percentage count of the proportion of your library which has seen each of the adapter sequences at each position.

Note that only samples with ≥ 0.1% adapter contamination are shown.

There may be several lines per sample, as one is shown for each adapter detected in the file.

From the FastQC Help:

The plot shows a cumulative percentage count of the proportion of your library which has seen each of the adapter sequences at each position. Once a sequence has been seen in a read it is counted as being present right through to the end of the read so the percentages you see will only increase as the read length goes on.

Created with MultiQC

Status Checks

Status for each FastQC section showing whether results seem entirely normal (green), slightly abnormal (orange) or very unusual (red).

FastQC assigns a status for each section of the report. These give a quick evaluation of whether the results of the analysis seem entirely normal (green), slightly abnormal (orange) or very unusual (red).

It is important to stress that although the analysis results appear to give a pass/fail result, these evaluations must be taken in the context of what you expect from your library. A 'normal' sample as far as FastQC is concerned is random and diverse. Some experiments may be expected to produce libraries which are biased in particular ways. You should treat the summary evaluations therefore as pointers to where you should concentrate your attention and understand why your library may not look random and diverse.

Specific guidance on how to interpret the output of each module can be found in the relevant report section, or in the FastQC help.

In this heatmap, we summarise all of these into a single heatmap for a quick overview. Note that not all FastQC sections have plots in MultiQC reports, but all status checks are shown in this heatmap.

Min:

Max:

Created with MultiQC

Cutadapt

Finds and removes adapter sequences, primers, poly-A tails, and other types of unwanted sequences.URL: https://cutadapt.readthedocs.ioDOI: 10.14806/ej.17.1.200

Filtered Reads

This plot shows the number of reads (SE) / pairs (PE) removed by Cutadapt.

Created with MultiQC

Trimmed Sequence Lengths (3')

This plot shows the number of reads with certain lengths of adapter trimmed for the 3' end.

Obs/Exp shows the raw counts divided by the number expected due to sequencing errors. A defined peak may be related to adapter length.

See the cutadapt documentation for more information on how these numbers are generated.

Created with MultiQC

FastQC (trimmed)

This section of the report shows FastQC results after adapter trimming.URL: http://www.bioinformatics.babraham.ac.uk/projects/fastqc

Sequence Counts

Sequence counts for each sample. Duplicate read counts are an estimate only.

This plot show the total number of reads, broken down into unique and duplicate if possible (only more recent versions of FastQC give duplicate info).

You can read more about duplicate calculation in the FastQC documentation. A small part has been copied here for convenience:

Only sequences which first appear in the first 100,000 sequences in each file are analysed. This should be enough to get a good impression for the duplication levels in the whole file. Each sequence is tracked to the end of the file to give a representative count of the overall duplication level.

The duplication detection requires an exact sequence match over the whole length of the sequence. Any reads over 75bp in length are truncated to 50bp for this analysis.

Created with MultiQC

Sequence Quality Histograms

60

0

0

60

0

0

60

0

0

The mean quality value across each base position in the read.

To enable multiple samples to be plotted on the same graph, only the mean quality scores are plotted (unlike the box plots seen in FastQC reports).

Taken from the FastQC help:

The y-axis on the graph shows the quality scores. The higher the score, the better the base call. The background of the graph divides the y axis into very good quality calls (green), calls of reasonable quality (orange), and calls of poor quality (red). The quality of calls on most platforms will degrade as the run progresses, so it is common to see base calls falling into the orange area towards the end of a read.

Created with MultiQC

Per Sequence Quality Scores

60

0

0

60

0

0

60

0

0

The number of reads with average quality scores. Shows if a subset of reads has poor quality.

From the FastQC help:

The per sequence quality score report allows you to see if a subset of your sequences have universally low quality values. It is often the case that a subset of sequences will have universally poor quality, however these should represent only a small percentage of the total sequences.

Created with MultiQC

Per Base Sequence Content

The proportion of each base position for which each of the four normal DNA bases has been called.

To enable multiple samples to be shown in a single plot, the base composition data is shown as a heatmap. The colours represent the balance between the four bases: an even distribution should give an even muddy brown colour. Hover over the plot to see the percentage of the four bases under the cursor.

To see the data as a line plot, as in the original FastQC graph, click on a sample track.

From the FastQC help:

Per Base Sequence Content plots out the proportion of each base position in a file for which each of the four normal DNA bases has been called.

In a random library you would expect that there would be little to no difference between the different bases of a sequence run, so the lines in this plot should run parallel with each other. The relative amount of each base should reflect the overall amount of these bases in your genome, but in any case they should not be hugely imbalanced from each other.

It's worth noting that some types of library will always produce biased sequence composition, normally at the start of the read. Libraries produced by priming using random hexamers (including nearly all RNA-Seq libraries) and those which were fragmented using transposases inherit an intrinsic bias in the positions at which reads start. This bias does not concern an absolute sequence, but instead provides enrichement of a number of different K-mers at the 5' end of the reads. Whilst this is a true technical bias, it isn't something which can be corrected by trimming and in most cases doesn't seem to adversely affect the downstream analysis.

Click a sample row to see a line plot for that dataset.

Rollover for sample name

Position: -

%T: -

%C: -

%A: -

%G: -

0

60

0

60

0

60

Per Sequence GC Content

5

34

21

5

34

21

5

34

21

The average GC content of reads. Normal random library typically have a roughly normal distribution of GC content.

From the FastQC help:

This module measures the GC content across the whole length of each sequence in a file and compares it to a modelled normal distribution of GC content.

In a normal random library you would expect to see a roughly normal distribution of GC content where the central peak corresponds to the overall GC content of the underlying genome. Since we don't know the GC content of the genome the modal GC content is calculated from the observed data and used to build a reference distribution.

An unusually shaped distribution could indicate a contaminated library or some other kinds of biased subset. A normal distribution which is shifted indicates some systematic bias which is independent of base position. If there is a systematic bias which creates a shifted normal distribution then this won't be flagged as an error by the module since it doesn't know what your genome's GC content should be.

Created with MultiQC

Per Base N Content

60

0

0

60

0

0

60

0

0

The percentage of base calls at each position for which an N was called.

From the FastQC help:

If a sequencer is unable to make a base call with sufficient confidence then it will normally substitute an N rather than a conventional base call. This graph shows the percentage of base calls at each position for which an N was called.

It's not unusual to see a very low proportion of Ns appearing in a sequence, especially nearer the end of a sequence. However, if this proportion rises above a few percent it suggests that the analysis pipeline was unable to interpret the data well enough to make valid base calls.

Created with MultiQC

Sequence Length Distribution

0

60

0

0

60

0

0

60

0

The distribution of fragment sizes (read lengths) found. See the FastQC help

Created with MultiQC

Sequence Duplication Levels

0

0

60

0

0

60

0

0

60

The relative level of duplication found for every sequence.

From the FastQC Help:

In a diverse library most sequences will occur only once in the final set. A low level of duplication may indicate a very high level of coverage of the target sequence, but a high level of duplication is more likely to indicate some kind of enrichment bias (e.g. PCR over amplification). This graph shows the degree of duplication for every sequence in a library: the relative number of sequences with different degrees of duplication.

Only sequences which first appear in the first 100,000 sequences in each file are analysed. This should be enough to get a good impression for the duplication levels in the whole file. Each sequence is tracked to the end of the file to give a representative count of the overall duplication level.

The duplication detection requires an exact sequence match over the whole length of the sequence. Any reads over 75bp in length are truncated to 50bp for this analysis.

In a properly diverse library most sequences should fall into the far left of the plot in both the red and blue lines. A general level of enrichment, indicating broad oversequencing in the library will tend to flatten the lines, lowering the low end and generally raising other categories. More specific enrichments of subsets, or the presence of low complexity contaminants will tend to produce spikes towards the right of the plot.

Created with MultiQC

Overrepresented sequences by sample

0

57

3

0

57

3

0

57

3

The total amount of overrepresented sequences found in each library.

FastQC calculates and lists overrepresented sequences in FastQ files. It would not be possible to show this for all samples in a MultiQC report, so instead this plot shows the number of sequences categorized as overrepresented.

Sometimes, a single sequence may account for a large number of reads in a dataset. To show this, the bars are split into two: the first shows the overrepresented reads that come from the single most common sequence. The second shows the total count from all remaining overrepresented sequences.

From the FastQC Help:

A normal high-throughput library will contain a diverse set of sequences, with no individual sequence making up a tiny fraction of the whole. Finding that a single sequence is very overrepresented in the set either means that it is highly biologically significant, or indicates that the library is contaminated, or not as diverse as you expected.

FastQC lists all the sequences which make up more than 0.1% of the total. To conserve memory only sequences which appear in the first 100,000 sequences are tracked to the end of the file. It is therefore possible that a sequence which is overrepresented but doesn't appear at the start of the file for some reason could be missed by this module.

Created with MultiQC

Top overrepresented sequences

Top overrepresented sequences across all samples. The table shows 20 most overrepresented sequences across all samples, ranked by the number of samples they occur in.

Showing ⁰/₂₀ rows and ³/₃ columns.

Overrepresented sequence	Reports	Occurrences	% of all reads
AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA	30	5207402	0.1903%
GGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGG	30	5853567	0.2139%
CCCCCCTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTT	21	1346024	0.0492%
CTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTT	19	1178989	0.0431%
TTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTT	10	586399	0.0214%
CCCCCCCTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTT	10	613819	0.0224%
GCTGGTTTGGGCTCCTCCGGTTTCGCTCGCCGCTACTCCCGGAATCGAGT	8	801816	0.0293%
CCCGGTTTGGGCTTATCCGGTTTCGCTCGCCGCTACTCCCGGAATCGAGT	8	2303714	0.0842%
CCCCCCCCTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTT	7	425019	0.0155%
CCCCCTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTT	6	380142	0.0139%
CCGGTTTGGGCTTATCCGGTTTCGCTCGCCGCTACTCCCGGAATCGAGTT	3	718814	0.0263%
TTTCGGTTTATCCATTCGTATTGGAATTTTTTTGAGAGATATTTTTCTCT	2	532550	0.0195%
CTTTTTTATTGATTGTCTTTATTTGAAACGCGCAATCTTCGTTTGCAACA	2	450668	0.0165%
CAGCCTTTTAAAGAGCCCAGTCTGTCATCGAGACGATCTTTAGCGTTTTG	2	119101	0.0044%
CTCAAGCCTTCTGTAGCTTTTCAGTTCTTTGTTTTTTGGATTCCACTCTT	2	278016	0.0102%
CCCGGTTTGGGCTGGCCCCCTTTCGCTCGCCGCTACTCGGGGGTTCGAGT	2	96363	0.0035%
GAAAAAAGCAAGGTACAAAAGATTGTTACATAGGATAACATCGAAGGACA	2	102078	0.0037%
GGCGGATGCCTTGGCACTGGGAGCCGAAGAAAGACGTGATAAGCTGCGAA	2	139723	0.0051%
CTCAACTTCTCGAAAGCATAAGAGGTGAACGAATAAACATCAGAAGTTAA	2	769662	0.0281%
GAAAAATCCAGGCACAATAAGCAGGAAAAAGAGCTGTTGCAAACGAAGAT	2	472649	0.0173%

Uncheck the tick box to hide columns. Click and drag the handle on the left to change order. Table ID: fastqc_trimmed_top_overrepresented_sequences_table_table

Sort	Column	Description	ID
\|\|	Reports	Number of FastQC reports where this sequence is founds as overrepresented	`fastqc_trimmed-samples`
\|\|	Occurrences	Total number of occurrences of the sequence (among the samples where the sequence is overrepresented)	`fastqc_trimmed-total_count`
\|\|	% of all reads	Total number of occurrences as the percentage of all reads (among samples where the sequence is overrepresented)	`fastqc_trimmed-total_percent`

Adapter Content

60

0

0

60

0

0

60

0

0

The cumulative percentage count of the proportion of your library which has seen each of the adapter sequences at each position.

Note that only samples with ≥ 0.1% adapter contamination are shown.

There may be several lines per sample, as one is shown for each adapter detected in the file.

From the FastQC Help:

The plot shows a cumulative percentage count of the proportion of your library which has seen each of the adapter sequences at each position. Once a sequence has been seen in a read it is counted as being present right through to the end of the read so the percentages you see will only increase as the read length goes on.

Created with MultiQC

Status Checks

Status for each FastQC section showing whether results seem entirely normal (green), slightly abnormal (orange) or very unusual (red).

FastQC assigns a status for each section of the report. These give a quick evaluation of whether the results of the analysis seem entirely normal (green), slightly abnormal (orange) or very unusual (red).

It is important to stress that although the analysis results appear to give a pass/fail result, these evaluations must be taken in the context of what you expect from your library. A 'normal' sample as far as FastQC is concerned is random and diverse. Some experiments may be expected to produce libraries which are biased in particular ways. You should treat the summary evaluations therefore as pointers to where you should concentrate your attention and understand why your library may not look random and diverse.

Specific guidance on how to interpret the output of each module can be found in the relevant report section, or in the FastQC help.

In this heatmap, we summarise all of these into a single heatmap for a quick overview. Note that not all FastQC sections have plots in MultiQC reports, but all status checks are shown in this heatmap.

Min:

Max:

Created with MultiQC

DupRadar

DupRadar provides duplication rate quality control for RNA-Seq datasets. Highly expressed genes can be expected to have a lot of duplicate reads, but high numbers of duplicates at low read counts can indicate low library complexity with technical duplication. This plot shows the general linear models - a summary of the gene duplication distributions.URL: bioconductor.org/packages/release/bioc/html/dupRadar.html

Created with MultiQC

Picard

Tools for manipulating high-throughput sequencing data.URL: http://broadinstitute.github.io/picard

Mark Duplicates

Number of reads, categorised by duplication state. Pair counts are doubled - see help text for details.

The table in the Picard metrics file contains some columns referring read pairs and some referring to single reads.

To make the numbers in this plot sum correctly, values referring to pairs are doubled according to the scheme below:

READS_IN_DUPLICATE_PAIRS = 2 * READ_PAIR_DUPLICATES
READS_IN_UNIQUE_PAIRS = 2 * (READ_PAIRS_EXAMINED - READ_PAIR_DUPLICATES)
READS_IN_UNIQUE_UNPAIRED = UNPAIRED_READS_EXAMINED - UNPAIRED_READ_DUPLICATES
READS_IN_DUPLICATE_PAIRS_OPTICAL = 2 * READ_PAIR_OPTICAL_DUPLICATES
READS_IN_DUPLICATE_PAIRS_NONOPTICAL = READS_IN_DUPLICATE_PAIRS - READS_IN_DUPLICATE_PAIRS_OPTICAL
READS_IN_DUPLICATE_UNPAIRED = UNPAIRED_READ_DUPLICATES
READS_UNMAPPED = UNMAPPED_READS

Created with MultiQC

QualiMap

Quality control of alignment data and its derivatives like feature counts.URL: http://qualimap.bioinfo.cipf.esDOI: 10.1093/bioinformatics/btv566; 10.1093/bioinformatics/bts503

Genomic origin of reads

Classification of mapped reads as originating in exonic, intronic or intergenic regions. These can be displayed as either the number or percentage of mapped reads.

There are currently three main approaches to map reads to transcripts in an RNA-seq experiment: mapping reads to a reference genome to identify expressed transcripts that are annotated (and discover those that are unknown), mapping reads to a reference transcriptome, and de novo assembly of transcript sequences (Conesa et al. 2016).

For RNA-seq QC analysis, QualiMap can be used to assess alignments produced by the first of these approaches. For input, it requires a GTF annotation file along with a reference genome, which can be used to reconstruct the exon structure of known transcripts. This allows mapped reads to be grouped by whether they originate in an exonic region (for QualiMap, this may include 5′ and 3′ UTR regions as well as protein-coding exons), an intron, or an intergenic region (see the Qualimap 2 documentation).

The inferred genomic origins of RNA-seq reads are presented here as a bar graph showing either the number or percentage of mapped reads in each read dataset that have been assigned to each type of genomic region. This graph can be used to assess the proportion of useful reads in an RNA-seq experiment. That proportion can be reduced by the presence of intron sequences, especially if depletion of ribosomal RNA was used during sample preparation (Sims et al. 2014). It can also be reduced by off-target transcripts, which are detected in greater numbers at the sequencing depths needed to detect poorly-expressed transcripts (Tarazona et al. 2011).

Created with MultiQC

Gene Coverage Profile

Mean distribution of coverage depth across the length of all mapped transcripts.

There are currently three main approaches to map reads to transcripts in an RNA-seq experiment: mapping reads to a reference genome to identify expressed transcripts that are annotated (and discover those that are unknown), mapping reads to a reference transcriptome, and de novo assembly of transcript sequences (Conesa et al. 2016).

For RNA-seq QC analysis, QualiMap can be used to assess alignments produced by the first of these approaches. For input, it requires a GTF annotation file along with a reference genome, which can be used to reconstruct the exon structure of known transcripts. QualiMap uses this information to calculate the depth of coverage along the length of each annotated transcript. For a set of reads mapped to a transcript, the depth of coverage at a given base position is the number of high-quality reads that map to the transcript at that position (Sims et al. 2014).

QualiMap calculates coverage depth at every base position of each annotated transcript. To enable meaningful comparison between transcripts, base positions are rescaled to relative positions expressed as percentage distance along each transcript (0%, 1%, …, 99%). For the set of transcripts with at least one mapped read, QualiMap plots the cumulative mapped-read depth (y-axis) at each relative transcript position (x-axis). This plot shows the gene coverage profile across all mapped transcripts for each read dataset. It provides a visual way to assess positional biases, such as an accumulation of mapped reads at the 3′ end of transcripts, which may indicate poor RNA quality in the original sample (Conesa et al. 2016).

The Normalised plot is calculated by MultiQC to enable comparison of samples with varying sequencing depth. The cumulative mapped-read depth at each position across the averaged transcript position are divided by the total for that sample across the entire averaged transcript.

Created with MultiQC

RSeQC

Evaluates high throughput RNA-seq data.URL: http://rseqc.sourceforge.netDOI: 10.1093/bioinformatics/bts356

Read Distribution

Read Distribution calculates how mapped reads are distributed over genome features.

Created with MultiQC

Inner Distance

Inner Distance calculates the inner distance (or insert size) between two paired RNA reads. Note that this can be negative if fragments overlap.

Created with MultiQC

Read Duplication

read_duplication.py calculates how many alignment positions have a certain number of exact duplicates. Note - plot truncated at 500 occurrences and binned.

Created with MultiQC

Junction Annotation

Junction annotation compares detected splice junctions to a reference gene model. An RNA read can be spliced 2 or more times, each time is called a splicing event.

Created with MultiQC

Junction Saturation

Junction Saturation counts the number of known splicing junctions that are observed in each dataset. If sequencing depth is sufficient, all (annotated) splice junctions should be rediscovered, resulting in a curve that reaches a plateau. Missing low abundance splice junctions can affect downstream analysis.

Click a line to see the data side by side (as in the original RSeQC plot).

Created with MultiQC

Infer experiment

Infer experiment counts the percentage of reads and read pairs that match the strandedness of overlapping transcripts. It can be used to infer whether RNA-seq library preps are stranded (sense or antisense).

Created with MultiQC

Bam Stat

All numbers reported in millions.

Created with MultiQC

Showing ⁰/₃₀ rows and ¹⁴/₁₄ columns.

Sample Name	Total records	QC failed	Duplicates	Non primary hit	Unmapped	Unique	Read-1	Read-2	+ve strand	-ve strand	Non-splice reads	Splice reads	Proper pairs	Different chrom
Control_SG37BCR	62.5M	0.0M	17.5M	3.4M	0.0M	40.3M	20.1M	20.1M	20.1M	20.1M	23.5M	16.7M	40.2M	0.0M
Control_SG40ACR	63.4M	0.0M	19.7M	3.7M	0.0M	38.7M	19.3M	19.3M	19.3M	19.3M	25.1M	13.6M	38.7M	0.0M
Control_SG68ACR	88.6M	0.0M	28.6M	5.0M	0.0M	53.2M	26.6M	26.6M	26.6M	26.6M	31.5M	21.7M	53.2M	0.0M
Control_SG6BCR	42.9M	0.0M	12.2M	3.1M	0.0M	26.5M	13.2M	13.2M	13.2M	13.2M	16.3M	10.2M	26.5M	0.0M
Control_SG7ACR	72.8M	0.0M	23.0M	5.5M	0.0M	42.5M	21.3M	21.3M	21.3M	21.3M	26.7M	15.9M	42.5M	0.0M
Control_SG86ACR	63.1M	0.0M	18.4M	3.1M	0.0M	40.3M	20.1M	20.1M	20.1M	20.1M	23.9M	16.4M	40.2M	0.0M
Park_SG109BCR	90.2M	0.0M	29.0M	5.4M	0.0M	53.9M	27.0M	27.0M	27.0M	27.0M	33.7M	20.3M	53.9M	0.0M
Park_SG17BCR	73.5M	0.0M	23.0M	3.7M	0.0M	45.3M	22.6M	22.6M	22.6M	22.6M	25.6M	19.7M	45.3M	0.0M
Park_SG18ACR	89.6M	0.0M	26.7M	4.5M	0.0M	56.6M	28.3M	28.3M	28.3M	28.3M	36.2M	20.4M	56.6M	0.0M
Park_SG23ACR	81.2M	0.0M	24.1M	4.4M	0.0M	51.1M	25.6M	25.6M	25.6M	25.6M	29.8M	21.3M	51.1M	0.0M
Park_SG48BCR	57.5M	0.0M	19.7M	3.8M	0.0M	32.7M	16.4M	16.4M	16.4M	16.4M	19.4M	13.3M	32.7M	0.0M
Park_SG53ACR	46.9M	0.0M	13.7M	3.0M	0.0M	29.1M	14.5M	14.5M	14.5M	14.5M	16.8M	12.3M	29.1M	0.0M
Park_SG73BCR	142.7M	0.0M	56.4M	11.0M	0.0M	72.4M	36.2M	36.2M	36.2M	36.2M	43.9M	28.5M	72.4M	0.0M
Park_SG90ACR	27.5M	0.0M	7.7M	2.0M	0.0M	17.1M	8.5M	8.5M	8.5M	8.5M	10.8M	6.3M	17.1M	0.0M
Park_SG94BCR	92.6M	0.0M	28.4M	4.9M	0.0M	57.4M	28.7M	28.7M	28.7M	28.7M	33.8M	23.5M	57.3M	0.0M
Park_SG95ACR	67.4M	0.0M	20.7M	3.6M	0.0M	41.6M	20.8M	20.8M	20.8M	20.8M	24.3M	17.3M	41.6M	0.0M
Urban_SG30ACR	64.2M	0.0M	19.7M	3.9M	0.0M	39.2M	19.6M	19.6M	19.6M	19.6M	22.4M	16.8M	39.2M	0.0M
Urban_SG32BCR	65.9M	0.0M	20.5M	4.2M	0.0M	39.6M	19.8M	19.8M	19.8M	19.8M	24.0M	15.6M	39.6M	0.0M
Urban_SG33BCR	69.4M	0.0M	20.1M	3.7M	0.0M	44.2M	22.1M	22.1M	22.1M	22.1M	26.2M	18.0M	44.2M	0.0M
Urban_SG34BCR	105.3M	0.0M	33.9M	6.2M	0.0M	63.1M	31.5M	31.5M	31.5M	31.5M	38.3M	24.8M	63.0M	0.0M
Urban_SG55BCR	75.8M	0.0M	24.3M	4.8M	0.0M	45.1M	22.5M	22.5M	22.5M	22.5M	29.8M	15.2M	45.1M	0.0M
Urban_SG56BCR	72.9M	0.0M	22.4M	4.4M	0.0M	44.5M	22.2M	22.3M	22.2M	22.2M	26.5M	18.0M	44.5M	0.0M
Urban_SG62ACR	85.8M	0.0M	27.6M	5.4M	0.0M	51.0M	25.5M	25.5M	25.5M	25.5M	29.6M	21.4M	51.0M	0.0M
Urban_SG64BCR	74.0M	0.0M	21.7M	5.3M	0.0M	45.1M	22.6M	22.6M	22.6M	22.6M	27.7M	17.5M	45.1M	0.0M
Urban_SG78ACR	31.9M	0.0M	9.0M	2.5M	0.0M	19.6M	9.8M	9.8M	9.8M	9.8M	11.6M	8.0M	19.6M	0.0M
Urban_SG79ACR	67.2M	0.0M	20.1M	4.4M	0.0M	41.1M	20.6M	20.6M	20.6M	20.6M	24.2M	17.0M	41.1M	0.0M
Urban_SG80BCR	70.4M	0.0M	22.7M	4.3M	0.0M	41.9M	20.9M	21.0M	21.0M	21.0M	27.4M	14.5M	41.9M	0.0M
Urban_SG82ACR	39.8M	0.0M	11.8M	2.5M	0.0M	24.6M	12.3M	12.3M	12.3M	12.3M	14.3M	10.3M	24.6M	0.0M
Urban_SG84ACR	80.0M	0.0M	24.8M	4.6M	0.0M	48.9M	24.5M	24.5M	24.5M	24.5M	27.6M	21.3M	48.9M	0.0M
Urban_SG97BCR	83.9M	0.0M	26.2M	5.3M	0.0M	50.6M	25.3M	25.3M	25.3M	25.3M	30.0M	20.6M	50.6M	0.0M

Uncheck the tick box to hide columns. Click and drag the handle on the left to change order. Table ID: rseqc_bam_stat_table-1

Sort	Column	Description	ID	Scale
\|\|	Total records	Total records	`total_records`	read_count
\|\|	QC failed	QC failed	`qc_failed`	read_count
\|\|	Duplicates	Optical/PCR duplicate	`optical_pcr_duplicate`	read_count
\|\|	Non primary hit	Non primary hit	`non_primary_hits`	read_count
\|\|	Unmapped	Unmapped reads	`unmapped_reads`	read_count
\|\|	Unique	mapq >= mapq_cut (unique)	`mapq_gte_mapq_cut_unique`	read_count
\|\|	Read-1	Read-1	`read_1`	read_count
\|\|	Read-2	Read-2	`read_2`	read_count
\|\|	+ve strand	Reads map to '+'	`reads_map_to_sense`	read_count
\|\|	-ve strand	Reads map to '-'	`reads_map_to_antisense`	read_count
\|\|	Non-splice reads	Non-splice reads	`non_splice_reads`	read_count
\|\|	Splice reads	Splice reads	`splice_reads`	read_count
\|\|	Proper pairs	Reads mapped in proper pairs	`reads_mapped_in_proper_pairs`	read_count
\|\|	Different chrom	Proper-paired reads map to different chrom	`proper_paired_reads_map_to_different_chrom`	read_count

Samtools

Toolkit for interacting with BAM/CRAM files.URL: http://www.htslib.orgDOI: 10.1093/bioinformatics/btp352

Percent mapped

Alignment metrics from samtools stats; mapped vs. unmapped reads vs. reads mapped with MQ0.

For a set of samples that have come from the same multiplexed library, similar numbers of reads for each sample are expected. Large differences in numbers might indicate issues during the library preparation process. Whilst large differences in read numbers may be controlled for in downstream processings (e.g. read count normalisation), you may wish to consider whether the read depths achieved have fallen below recommended levels depending on the applications.

Low alignment rates could indicate contamination of samples (e.g. adapter sequences), low sequencing quality or other artefacts. These can be further investigated in the sequence level QC (e.g. from FastQC).

Reads mapped with MQ0 often indicate that the reads are ambiguously mapped to multiple locations in the reference sequence. This can be due to repetitive regions in the genome, the presence of alternative contigs in the reference, or due to reads that are too short to be uniquely mapped. These reads are often filtered out in downstream analyses.

Created with MultiQC

Alignment stats

This module parses the output from samtools stats. All numbers in millions.

Created with MultiQC

Showing ⁰/₃₀ rows and ¹³/₁₃ columns.

Sample Name	Total sequences	Mapped & paired	Properly paired	Duplicated	QC Failed	Reads MQ0	Mapped bases (CIGAR)	Bases Trimmed	Duplicated bases	Diff chromosomes	Other orientation	Inward pairs	Outward pairs
Control_SG37BCR	59.1M	59.1M	59.1M	17.5M	0.0M	0.1M	8421.8Mb	0.0Mb	2545.4Mb	0.0M	0.0M	26.1M	3.4M
Control_SG40ACR	59.7M	59.6M	59.6M	0.0M	0.0M	0.1M	8510.6Mb	0.0Mb	0.0Mb	0.0M	0.0M	26.4M	3.4M
Control_SG68ACR	83.6M	83.4M	83.4M	0.0M	0.0M	0.1M	11908.6Mb	0.0Mb	0.0Mb	0.0M	0.0M	37.0M	4.7M
Control_SG6BCR	39.7M	39.7M	39.7M	12.2M	0.0M	0.1M	5670.2Mb	0.0Mb	1774.7Mb	0.0M	0.0M	17.6M	2.2M
Control_SG7ACR	67.3M	67.2M	67.2M	23.0M	0.0M	0.2M	9425.9Mb	0.0Mb	3284.9Mb	0.0M	0.0M	28.7M	4.9M
Control_SG86ACR	59.9M	59.9M	59.9M	18.4M	0.0M	0.1M	8591.4Mb	0.0Mb	2686.6Mb	0.0M	0.0M	26.8M	3.1M
Park_SG109BCR	84.8M	84.6M	84.6M	0.0M	0.0M	0.1M	12128.1Mb	0.0Mb	0.0Mb	0.0M	0.0M	37.9M	4.5M
Park_SG17BCR	69.8M	69.6M	69.6M	0.0M	0.0M	0.1M	10119.6Mb	0.0Mb	0.0Mb	0.0M	0.0M	32.3M	2.5M
Park_SG18ACR	85.1M	84.9M	84.9M	0.0M	0.0M	0.1M	12140.6Mb	0.0Mb	0.0Mb	0.0M	0.0M	37.9M	4.6M
Park_SG23ACR	76.8M	76.7M	76.7M	24.1M	0.0M	0.1M	11016.5Mb	0.0Mb	3508.4Mb	0.0M	0.0M	34.5M	3.9M
Park_SG48BCR	53.7M	53.6M	53.6M	19.7M	0.0M	0.1M	7659.0Mb	0.0Mb	2856.1Mb	0.0M	0.0M	23.8M	3.0M
Park_SG53ACR	43.9M	43.8M	43.8M	0.0M	0.0M	0.1M	6260.2Mb	0.0Mb	0.0Mb	0.0M	0.0M	19.5M	2.4M
Park_SG73BCR	131.7M	131.4M	131.4M	0.0M	0.0M	0.4M	18330.0Mb	0.0Mb	0.0Mb	0.0M	0.0M	55.2M	10.5M
Park_SG90ACR	25.4M	25.4M	25.4M	7.7M	0.0M	0.1M	3641.0Mb	0.0Mb	1117.4Mb	0.0M	0.0M	11.4M	1.3M
Park_SG94BCR	87.6M	87.5M	87.5M	28.4M	0.0M	0.1M	12491.6Mb	0.0Mb	4122.7Mb	0.0M	0.0M	38.7M	5.0M
Park_SG95ACR	63.7M	63.6M	63.6M	0.0M	0.0M	0.1M	8989.1Mb	0.0Mb	0.0Mb	0.0M	0.0M	27.7M	4.1M
Urban_SG30ACR	60.3M	60.2M	60.2M	19.7M	0.0M	0.1M	8609.9Mb	0.0Mb	2858.2Mb	0.0M	0.0M	26.9M	3.2M
Urban_SG32BCR	61.6M	61.5M	61.5M	20.5M	0.0M	0.1M	8691.4Mb	0.0Mb	2952.3Mb	0.0M	0.0M	26.8M	3.9M
Urban_SG33BCR	65.7M	65.6M	65.6M	0.0M	0.0M	0.1M	9436.6Mb	0.0Mb	0.0Mb	0.0M	0.0M	29.7M	3.1M
Urban_SG34BCR	99.1M	98.9M	98.9M	0.0M	0.0M	0.2M	13940.0Mb	0.0Mb	0.0Mb	0.0M	0.0M	42.5M	7.0M
Urban_SG55BCR	71.0M	70.8M	70.8M	24.3M	0.0M	0.1M	10149.9Mb	0.0Mb	3542.3Mb	0.0M	0.0M	31.7M	3.8M
Urban_SG56BCR	68.5M	68.4M	68.4M	22.4M	0.0M	0.1M	9761.0Mb	0.0Mb	3253.6Mb	0.0M	0.0M	30.2M	4.0M
Urban_SG62ACR	80.5M	80.3M	80.3M	27.6M	0.0M	0.2M	11452.2Mb	0.0Mb	4003.4Mb	0.0M	0.0M	35.6M	4.6M
Urban_SG64BCR	68.7M	68.5M	68.5M	0.0M	0.0M	0.2M	9444.2Mb	0.0Mb	0.0Mb	0.0M	0.0M	28.4M	5.9M
Urban_SG78ACR	29.4M	29.3M	29.3M	9.0M	0.0M	0.1M	4136.3Mb	0.0Mb	1289.1Mb	0.0M	0.0M	12.6M	2.0M
Urban_SG79ACR	62.8M	62.8M	62.8M	0.0M	0.0M	0.1M	8982.8Mb	0.0Mb	0.0Mb	0.0M	0.0M	27.8M	3.6M
Urban_SG80BCR	66.1M	65.9M	65.9M	0.0M	0.0M	0.1M	9436.1Mb	0.0Mb	0.0Mb	0.0M	0.0M	29.3M	3.7M
Urban_SG82ACR	37.3M	37.2M	37.2M	0.0M	0.0M	0.1M	5300.5Mb	0.0Mb	0.0Mb	0.0M	0.0M	16.4M	2.2M
Urban_SG84ACR	75.4M	75.3M	75.3M	24.8M	0.0M	0.1M	10663.8Mb	0.0Mb	3580.4Mb	0.0M	0.0M	32.9M	4.7M
Urban_SG97BCR	78.7M	78.5M	78.5M	0.0M	0.0M	0.2M	11070.7Mb	0.0Mb	0.0Mb	0.0M	0.0M	34.0M	5.3M

Uncheck the tick box to hide columns. Click and drag the handle on the left to change order. Table ID: samtools-stats-dp_table-1

Sort	Column	Description	ID	Scale
\|\|	Total sequences	Total sequences	`raw_total_sequences`	read_count
\|\|	Mapped & paired	Paired-end technology bit set + both mates mapped	`reads_mapped_and_paired`	read_count
\|\|	Properly paired	Proper-pair bit set	`reads_properly_paired`	read_count
\|\|	Duplicated	PCR or optical duplicate bit set	`reads_duplicated`	read_count
\|\|	QC Failed	QC Failed	`reads_QC_failed`	read_count
\|\|	Reads MQ0	Reads mapped and MQ=0	`reads_MQ0`	read_count
\|\|	Mapped bases (CIGAR)	Mapped bases (CIGAR)	`bases_mapped__cigar`	base_count
\|\|	Bases Trimmed	Bases Trimmed	`bases_trimmed`	base_count
\|\|	Duplicated bases	Duplicated bases	`bases_duplicated`	base_count
\|\|	Diff chromosomes	Pairs on different chromosomes	`pairs_on_different_chromosomes`	read_count
\|\|	Other orientation	Pairs with other orientation	`pairs_with_other_orientation`	read_count
\|\|	Inward pairs	Inward oriented pairs	`inward_oriented_pairs`	read_count
\|\|	Outward pairs	Outward oriented pairs	`outward_oriented_pairs`	read_count

Flagstat

This module parses the output from samtools flagstat

Created with MultiQC

Showing ⁰/₃₀ rows and ¹¹/₁₁ columns.

Sample Name	Total Reads	Total Passed QC	Mapped	Secondary Alignments	Duplicates	Paired in Sequencing	Properly Paired	Self and mate mapped	Singletons	Mate mapped to diff chr	Diff chr (mapQ >= 5)
Control_SG37BCR	62.5M	62.5M	62.5M	3.4M	0.0M	59.1M	59.1M	59.1M	0.1M	0.0M	0.0M
Control_SG40ACR	63.4M	63.4M	63.4M	3.7M	19.7M	59.7M	59.6M	59.6M	0.1M	0.0M	0.0M
Control_SG68ACR	88.6M	88.6M	88.6M	5.0M	0.0M	83.6M	83.4M	83.4M	0.2M	0.0M	0.0M
Control_SG6BCR	42.9M	42.9M	42.9M	3.1M	0.0M	39.7M	39.7M	39.7M	0.1M	0.0M	0.0M
Control_SG7ACR	72.8M	72.8M	72.8M	5.5M	23.0M	67.3M	67.2M	67.2M	0.1M	0.0M	0.0M
Control_SG86ACR	63.1M	63.1M	63.1M	3.1M	0.0M	59.9M	59.9M	59.9M	0.1M	0.0M	0.0M
Park_SG109BCR	90.2M	90.2M	90.2M	5.4M	0.0M	84.8M	84.6M	84.6M	0.2M	0.0M	0.0M
Park_SG17BCR	73.5M	73.5M	73.5M	3.7M	0.0M	69.8M	69.6M	69.6M	0.1M	0.0M	0.0M
Park_SG18ACR	89.6M	89.6M	89.6M	4.5M	0.0M	85.1M	84.9M	84.9M	0.1M	0.0M	0.0M
Park_SG23ACR	81.2M	81.2M	81.2M	4.4M	0.0M	76.8M	76.7M	76.7M	0.1M	0.0M	0.0M
Park_SG48BCR	57.5M	57.5M	57.5M	3.8M	0.0M	53.7M	53.6M	53.6M	0.1M	0.0M	0.0M
Park_SG53ACR	46.9M	46.9M	46.9M	3.0M	13.7M	43.9M	43.8M	43.8M	0.1M	0.0M	0.0M
Park_SG73BCR	142.7M	142.7M	142.7M	11.0M	0.0M	131.7M	131.4M	131.4M	0.3M	0.0M	0.0M
Park_SG90ACR	27.5M	27.5M	27.5M	2.0M	0.0M	25.4M	25.4M	25.4M	0.0M	0.0M	0.0M
Park_SG94BCR	92.6M	92.6M	92.6M	4.9M	28.4M	87.6M	87.5M	87.5M	0.2M	0.0M	0.0M
Park_SG95ACR	67.4M	67.4M	67.4M	3.6M	20.7M	63.7M	63.6M	63.6M	0.1M	0.0M	0.0M
Urban_SG30ACR	64.2M	64.2M	64.2M	3.9M	0.0M	60.3M	60.2M	60.2M	0.1M	0.0M	0.0M
Urban_SG32BCR	65.9M	65.9M	65.9M	4.2M	20.5M	61.6M	61.5M	61.5M	0.1M	0.0M	0.0M
Urban_SG33BCR	69.4M	69.4M	69.4M	3.7M	0.0M	65.7M	65.6M	65.6M	0.1M	0.0M	0.0M
Urban_SG34BCR	105.3M	105.3M	105.3M	6.2M	0.0M	99.1M	98.9M	98.9M	0.2M	0.0M	0.0M
Urban_SG55BCR	75.8M	75.8M	75.8M	4.8M	24.3M	71.0M	70.8M	70.8M	0.1M	0.0M	0.0M
Urban_SG56BCR	72.9M	72.9M	72.9M	4.4M	0.0M	68.5M	68.4M	68.4M	0.1M	0.0M	0.0M
Urban_SG62ACR	85.8M	85.8M	85.8M	5.4M	0.0M	80.5M	80.3M	80.3M	0.2M	0.0M	0.0M
Urban_SG64BCR	74.0M	74.0M	74.0M	5.3M	0.0M	68.7M	68.5M	68.5M	0.1M	0.0M	0.0M
Urban_SG78ACR	31.9M	31.9M	31.9M	2.5M	0.0M	29.4M	29.3M	29.3M	0.1M	0.0M	0.0M
Urban_SG79ACR	67.2M	67.2M	67.2M	4.4M	0.0M	62.8M	62.8M	62.8M	0.1M	0.0M	0.0M
Urban_SG80BCR	70.4M	70.4M	70.4M	4.3M	0.0M	66.1M	65.9M	65.9M	0.1M	0.0M	0.0M
Urban_SG82ACR	39.8M	39.8M	39.8M	2.5M	11.8M	37.3M	37.2M	37.2M	0.1M	0.0M	0.0M
Urban_SG84ACR	80.0M	80.0M	80.0M	4.6M	0.0M	75.4M	75.3M	75.3M	0.1M	0.0M	0.0M
Urban_SG97BCR	83.9M	83.9M	83.9M	5.3M	0.0M	78.7M	78.5M	78.5M	0.1M	0.0M	0.0M

Uncheck the tick box to hide columns. Click and drag the handle on the left to change order. Table ID: samtools-flagstat-dp_table-1

Sort	Column	Description	ID	Scale
\|\|	Total Reads	Total Reads	`flagstat_total`	read_count
\|\|	Total Passed QC	Total Passed QC	`total_passed`	read_count
\|\|	Mapped	Mapped	`mapped_passed`	read_count
\|\|	Secondary Alignments	Secondary Alignments	`secondary_passed`	read_count
\|\|	Duplicates	Duplicates	`duplicates_passed`	read_count
\|\|	Paired in Sequencing	Paired in Sequencing	`paired_in_sequencing_passed`	read_count
\|\|	Properly Paired	Properly Paired	`properly_paired_passed`	read_count
\|\|	Self and mate mapped	Reads with itself and mate mapped	`with_itself_and_mate_mapped_passed`	read_count
\|\|	Singletons	Singletons	`singletons_passed`	read_count
\|\|	Mate mapped to diff chr	Mate mapped to different chromosome	`with_mate_mapped_to_a_different_chr_passed`	read_count
\|\|	Diff chr (mapQ >= 5)	Mate mapped to different chromosome (mapQ >= 5)	`with_mate_mapped_to_a_different_chr_mapQ_5__passed`	read_count

Mapped reads per contig

The samtools idxstats tool counts the number of mapped reads per chromosome / contig. Chromosomes with < 0.1% of the total aligned reads are omitted from this plot.

Created with MultiQC

STAR

Universal RNA-seq aligner.URL: https://github.com/alexdobin/STARDOI: 10.1093/bioinformatics/bts635

Summary Statistics

Summary statistics from the STAR alignment

Showing ⁰/₃₀ rows and ¹⁰/₁₉ columns.

Sample Name	Total reads	Aligned	Aligned	Uniq aligned	Uniq aligned	Multimapped	Avg. read len	Avg. mapped len	Splices	Annotated splices	GT/AG splices	GC/AG splices	AT/AC splices	Non-canonical splices	Mismatch rate	Del rate	Del len	Ins rate	Ins len
Control_SG37BCR	32.1M	29.6M	92.3%	28.5M	88.7%	1.2M	286.0bp	284.6bp	29.9M	29.9M	29.6M	0.3M	0.0M	0.1M	0.6%	0.0%	2.4bp	0.0%	2.1bp
Control_SG40ACR	36.3M	29.9M	82.3%	28.5M	78.6%	1.4M	285.0bp	285.1bp	23.8M	23.8M	23.5M	0.2M	0.0M	0.0M	0.7%	0.0%	2.5bp	0.0%	2.1bp
Control_SG68ACR	48.6M	41.9M	86.1%	40.2M	82.6%	1.7M	285.0bp	284.6bp	41.8M	41.8M	41.3M	0.4M	0.0M	0.1M	0.6%	0.0%	2.5bp	0.0%	2.0bp
Control_SG6BCR	34.0M	19.9M	58.5%	19.0M	55.7%	0.9M	284.0bp	285.2bp	18.8M	18.8M	18.6M	0.1M	0.0M	0.0M	0.6%	0.0%	2.5bp	0.0%	2.1bp
Control_SG7ACR	41.4M	33.7M	81.5%	32.0M	77.4%	1.7M	280.0bp	280.1bp	31.1M	31.0M	30.7M	0.3M	0.0M	0.1M	0.6%	0.0%	2.5bp	0.0%	2.0bp
Control_SG86ACR	34.6M	30.0M	86.8%	28.9M	83.7%	1.1M	287.0bp	286.4bp	29.5M	29.5M	29.1M	0.3M	0.0M	0.1M	0.6%	0.0%	2.5bp	0.0%	2.0bp
Park_SG109BCR	47.0M	42.5M	90.5%	40.4M	86.1%	2.1M	287.0bp	285.8bp	35.9M	35.9M	35.5M	0.4M	0.0M	0.1M	0.6%	0.0%	2.5bp	0.0%	2.1bp
Park_SG17BCR	39.5M	35.0M	88.4%	33.6M	85.1%	1.3M	291.0bp	289.7bp	36.9M	36.8M	36.4M	0.3M	0.0M	0.1M	0.6%	0.0%	2.4bp	0.0%	2.0bp
Park_SG18ACR	46.6M	42.6M	91.5%	41.0M	88.0%	1.6M	287.0bp	285.3bp	36.3M	36.2M	35.8M	0.3M	0.0M	0.1M	0.6%	0.0%	2.5bp	0.0%	2.1bp
Park_SG23ACR	41.5M	38.5M	92.7%	37.0M	89.1%	1.5M	288.0bp	286.5bp	39.1M	39.1M	38.7M	0.4M	0.0M	0.1M	0.6%	0.0%	2.5bp	0.0%	2.0bp
Park_SG48BCR	37.2M	26.9M	72.4%	25.7M	69.0%	1.3M	284.0bp	285.0bp	27.3M	27.2M	27.0M	0.2M	0.0M	0.0M	0.6%	0.0%	2.3bp	0.0%	2.0bp
Park_SG53ACR	35.6M	22.0M	61.8%	21.0M	59.1%	1.0M	283.0bp	285.2bp	22.7M	22.7M	22.4M	0.2M	0.0M	0.0M	0.6%	0.0%	2.4bp	0.0%	2.0bp
Park_SG73BCR	79.8M	66.0M	82.7%	62.5M	78.3%	3.5M	278.0bp	278.5bp	63.2M	63.1M	62.5M	0.5M	0.1M	0.1M	0.6%	0.0%	2.4bp	0.0%	2.1bp
Park_SG90ACR	25.6M	12.7M	49.7%	12.1M	47.2%	0.6M	282.0bp	286.2bp	10.9M	10.9M	10.8M	0.1M	0.0M	0.0M	0.6%	0.0%	2.5bp	0.0%	2.1bp
Park_SG94BCR	47.1M	43.9M	93.2%	42.2M	89.6%	1.7M	286.0bp	284.8bp	43.7M	43.7M	43.2M	0.4M	0.0M	0.1M	0.6%	0.0%	2.5bp	0.0%	2.0bp
Park_SG95ACR	35.2M	31.9M	90.7%	30.7M	87.1%	1.2M	283.0bp	282.0bp	32.4M	32.4M	32.0M	0.3M	0.0M	0.1M	0.6%	0.0%	2.5bp	0.0%	2.0bp
Urban_SG30ACR	40.2M	30.2M	75.2%	28.9M	72.0%	1.3M	286.0bp	285.4bp	31.4M	31.4M	31.0M	0.3M	0.0M	0.1M	0.6%	0.0%	2.4bp	0.0%	2.0bp
Urban_SG32BCR	34.4M	30.9M	89.7%	29.5M	85.7%	1.4M	283.0bp	281.8bp	29.7M	29.7M	29.4M	0.2M	0.0M	0.1M	0.6%	0.0%	2.4bp	0.0%	2.0bp
Urban_SG33BCR	36.5M	32.9M	90.1%	31.7M	86.7%	1.2M	288.0bp	286.9bp	32.8M	32.8M	32.4M	0.3M	0.0M	0.1M	0.6%	0.0%	2.5bp	0.0%	2.0bp
Urban_SG34BCR	54.5M	49.6M	91.1%	47.5M	87.2%	2.1M	283.0bp	281.3bp	46.6M	46.5M	46.0M	0.4M	0.0M	0.1M	0.6%	0.0%	2.5bp	0.0%	2.1bp
Urban_SG55BCR	44.0M	35.5M	80.7%	33.8M	76.9%	1.7M	286.0bp	285.9bp	27.1M	27.1M	26.7M	0.3M	0.0M	0.0M	0.7%	0.0%	2.5bp	0.0%	2.1bp
Urban_SG56BCR	39.0M	34.3M	88.1%	32.9M	84.3%	1.5M	285.0bp	284.7bp	33.9M	33.8M	33.5M	0.3M	0.0M	0.1M	0.5%	0.0%	2.4bp	0.0%	2.0bp
Urban_SG62ACR	49.8M	40.3M	80.9%	38.6M	77.5%	1.7M	285.0bp	284.5bp	41.4M	41.3M	40.9M	0.4M	0.0M	0.1M	0.6%	0.0%	2.5bp	0.0%	2.0bp
Urban_SG64BCR	40.3M	34.4M	85.3%	32.8M	81.3%	1.6M	274.0bp	275.3bp	32.7M	32.7M	32.3M	0.3M	0.0M	0.1M	0.5%	0.0%	2.5bp	0.0%	2.0bp
Urban_SG78ACR	38.8M	14.7M	38.0%	14.0M	36.1%	0.7M	279.0bp	281.2bp	14.6M	14.6M	14.5M	0.1M	0.0M	0.0M	0.5%	0.0%	2.5bp	0.0%	2.0bp
Urban_SG79ACR	33.9M	31.4M	92.9%	30.0M	88.6%	1.5M	287.0bp	286.0bp	31.2M	31.2M	30.9M	0.3M	0.0M	0.1M	0.6%	0.0%	2.4bp	0.0%	2.2bp
Urban_SG80BCR	36.5M	33.1M	90.6%	31.5M	86.4%	1.5M	287.0bp	285.6bp	25.9M	25.9M	25.6M	0.2M	0.0M	0.0M	0.6%	0.0%	2.6bp	0.0%	2.1bp
Urban_SG82ACR	27.1M	18.7M	69.0%	17.9M	65.9%	0.8M	283.0bp	283.9bp	18.6M	18.6M	18.4M	0.2M	0.0M	0.0M	0.6%	0.0%	2.4bp	0.0%	2.1bp
Urban_SG84ACR	47.5M	37.8M	79.5%	36.2M	76.2%	1.5M	283.0bp	282.7bp	39.7M	39.7M	39.3M	0.4M	0.0M	0.1M	0.6%	0.0%	2.6bp	0.0%	2.0bp
Urban_SG97BCR	44.7M	39.4M	88.2%	37.7M	84.4%	1.7M	282.0bp	281.3bp	39.3M	39.3M	38.9M	0.3M	0.0M	0.1M	0.6%	0.0%	2.5bp	0.0%	2.0bp

Uncheck the tick box to hide columns. Click and drag the handle on the left to change order. Table ID: star_summary_table_table

Sort	Column	Description	ID	Scale
\|\|	Total reads	Number of input reads	`star-total_reads`	read_count
\|\|	Aligned	Mapped reads	`star-mapped`	read_count
\|\|	Aligned	% Mapped reads	`star-mapped_percent`
\|\|	Uniq aligned	Uniquely mapped reads	`star-uniquely_mapped`	read_count
\|\|	Uniq aligned	% Uniquely mapped reads	`star-uniquely_mapped_percent`
\|\|	Multimapped	Multiple mapped reads	`star-multimapped`	read_count
\|\|	Avg. read len	Average input read length	`star-avg_input_read_length`
\|\|	Avg. mapped len	Average mapped length	`star-avg_mapped_read_length`
\|\|	Splices	Number of splices: Total	`star-num_splices`	read_count
\|\|	Annotated splices	Number of splices: Annotated (sjdb)	`star-num_annotated_splices`	read_count
\|\|	GT/AG splices	Number of splices: GT/AG	`star-num_GTAG_splices`	read_count
\|\|	GC/AG splices	Number of splices: GC/AG	`star-num_GCAG_splices`	read_count
\|\|	AT/AC splices	Number of splices: AT/AC	`star-num_ATAC_splices`	read_count
\|\|	Non-canonical splices	Number of splices: Non-canonical	`star-num_noncanonical_splices`	read_count
\|\|	Mismatch rate	Mismatch rate per base	`star-mismatch_rate`
\|\|	Del rate	Deletion rate per base	`star-deletion_rate`
\|\|	Del len	Deletion average length	`star-deletion_length`
\|\|	Ins rate	Insertion rate per base	`star-insertion_rate`
\|\|	Ins len	Insertion average length	`star-insertion_length`

Alignment Scores

Created with MultiQC

Sample relationships

Plots interrogating sample relationships, based on final count matrices.

STAR_SALMON DESeq2 sample similarity

Min:

Max:

Created with MultiQC

STAR_SALMON DESeq2 PCA plot

Created with MultiQC

SortMeRNA

Program for filtering, mapping and OTU-picking NGS reads in metatranscriptomic and metagenomic data.URL: http://bioinfo.lifl.fr/RNA/sortmernaDOI: 10.1093/bioinformatics/bts611

The core algorithm is based on approximate seeds and allows for fast and sensitive analyses of nucleotide sequences. The main application of SortMeRNA is filtering ribosomal RNA from metatranscriptomic data.

Created with MultiQC

Software Versions

Software Versions lists versions of software tools extracted from file contents.

Group	Software	Version
BEDTOOLS_GENOMECOV_FW	bedtools	`2.31.1`
CUSTOM_GETCHROMSIZES	getchromsizes	`1.21`
CUSTOM_TX2GENE	python	`3.10.4`
DESEQ2_QC_STAR_SALMON	bioconductor-deseq2	`1.28.0`
	r-base	`4.0.3`
DupRadar	bioconductor-dupradar	`1.32.0`
FASTQC	fastqc	`0.12.1`
FQ_LINT	fq	`0.12.0 (2024-07-08)`
FQ_SUBSAMPLE	fq	`0.12.0 (2024-07-08)`
GTF2BED	perl	`5.26.2`
GTF_FILTER	python	`3.9.5`
MAKE_TRANSCRIPTS_FASTA	rsem	`1.3.1`
	star	`2.7.10a`
PICARD_MARKDUPLICATES	picard	`3.1.1`
QUALIMAP_RNASEQ	qualimap	`2.3`
RSEQC_BAMSTAT	rseqc	`5.0.2`
RSEQC_INFEREXPERIMENT	rseqc	`5.0.2`
RSEQC_INNERDISTANCE	rseqc	`5.0.2`
RSEQC_JUNCTIONANNOTATION	rseqc	`5.0.2`
RSEQC_JUNCTIONSATURATION	rseqc	`5.0.2`
RSEQC_READDISTRIBUTION	rseqc	`5.0.2`
RSEQC_READDUPLICATION	rseqc	`5.0.2`
SALMON_INDEX	salmon	`1.10.3`
SALMON_QUANT	salmon	`1.10.3`
SAMTOOLS_FLAGSTAT	samtools	`1.21`
SAMTOOLS_IDXSTATS	samtools	`1.21`
SAMTOOLS_INDEX	samtools	`1.21`
SAMTOOLS_SORT	samtools	`1.21`
SAMTOOLS_STATS	samtools	`1.21`
SE_GENE	bioconductor-summarizedexperiment	`1.32.0`
SORTMERNA_INDEX	sortmerna	`4.3.7`
STAR_ALIGN	gawk	`5.1.0`
	samtools	`1.21`
	star	`2.7.11b`
STAR_GENOMEGENERATE	gawk	`5.1.0`
	samtools	`1.21`
	star	`2.7.11b`
STRINGTIE_STRINGTIE	stringtie	`2.2.3`
SortMeRNA	sortmerna	`4.3.7`
TRIMGALORE	cutadapt	`4.9`
	trimgalore	`0.6.10`
TXIMETA_TXIMPORT	bioconductor-tximeta	`1.20.1`
UCSC_BEDCLIP	ucsc	`377`
UCSC_BEDGRAPHTOBIGWIG	ucsc	`469`
Workflow	Nextflow	`24.10.3`
	nf-core/rnaseq	`v3.18.0-gb96a753`

nf-core/rnaseq Methods Description

Suggested text and references to use when describing pipeline usage within the methods section of a publication.URL: https://github.com/nf-core/rnaseq

Methods

Data was processed using nf-core/rnaseq v3.18.0 (doi: 10.5281/zenodo.1400710) of the nf-core collection of workflows (Ewels et al., 2020), utilising reproducible software environments from the Bioconda (Grüning et al., 2018) and Biocontainers (da Veiga Leprevost et al., 2017) projects.

The pipeline was executed with Nextflow v24.10.3 (Di Tommaso et al., 2017) with the following command:

nextflow run nf-core/rnaseq -r 3.18.0 -profile apptainer -work-dir /scratch/project_2003826/SOIL2GUT_RNA/work -params-file /scratch/project_2003826/SOIL2GUT_RNA/nf-params.json --igenomes_ignore -resume

References

Di Tommaso, P., Chatzou, M., Floden, E. W., Barja, P. P., Palumbo, E., & Notredame, C. (2017). Nextflow enables reproducible computational workflows. Nature Biotechnology, 35(4), 316-319. doi: 10.1038/nbt.3820
Ewels, P. A., Peltzer, A., Fillinger, S., Patel, H., Alneberg, J., Wilm, A., Garcia, M. U., Di Tommaso, P., & Nahnsen, S. (2020). The nf-core framework for community-curated bioinformatics pipelines. Nature Biotechnology, 38(3), 276-278. doi: 10.1038/s41587-020-0439-x
Grüning, B., Dale, R., Sjödin, A., Chapman, B. A., Rowe, J., Tomkins-Tinch, C. H., Valieris, R., Köster, J., & Bioconda Team. (2018). Bioconda: sustainable and comprehensive software distribution for the life sciences. Nature Methods, 15(7), 475–476. doi: 10.1038/s41592-018-0046-7
da Veiga Leprevost, F., Grüning, B. A., Alves Aflitos, S., Röst, H. L., Uszkoreit, J., Barsnes, H., Vaudel, M., Moreno, P., Gatto, L., Weber, J., Bai, M., Jimenez, R. C., Sachsenberg, T., Pfeuffer, J., Vera Alvarez, R., Griss, J., Nesvizhskii, A. I., & Perez-Riverol, Y. (2017). BioContainers: an open-source and community-driven framework for software standardization. Bioinformatics (Oxford, England), 33(16), 2580–2582. doi: 10.1093/bioinformatics/btx192

Notes:

The command above does not include parameters contained in any configs or profiles that may have been used. Ensure the config file is also uploaded with your publication!
You should also cite all software used within this run. Check the "Software Versions" of this report to get version information.

nf-core/rnaseq Workflow Summary

- this information is collected when the pipeline is started.URL: https://github.com/nf-core/rnaseq

Input/output options

email: redacted
input: /scratch/project_2003826/samplesheet.csv
multiqc_title: redacted
outdir: /scratch/project_2003826/results

Reference genome options

fasta: /scratch/project_2003826/reference/genome.fa
gtf: /scratch/project_2003826/reference/annotation.gtf
igenomes_ignore: true

Read filtering options

remove_ribo_rna: true

Core Nextflow options

configFiles: N/A
containerEngine: apptainer
launchDir: /scratch/project_2003826/
profile: apptainer
projectDir: /users/tjernfor/.nextflow/assets/nf-core/rnaseq
revision: 3.18.0
runName: stupefied_hilbert
userName: tjernfor
workDir: /scratch/project_2003826/work

Toggle navigation v1.25.1

MultiQC Toolbox

Apply Highlight Samples

Apply Rename Samples

Apply Show / Hide Samples

Export Plots

Choose Plots

Save Settings

Load Settings

Tool Citations

About MultiQC

General Statistics

General Statistics: Columns

Sample status checks

Strandedness Checks

Samples strandedness check: Columns

FastQC (raw)

Sequence Counts Help

Sequence Quality Histograms Help 60 0 0 60 0 0 60 0 0

Per Sequence Quality Scores Help 60 0 0 60 0 0 60 0 0

Per Base Sequence Content Help

Rollover for sample name

Per Sequence GC Content Help 7 36 17 7 36 17 7 36 17

Per Base N Content Help 60 0 0 60 0 0 60 0 0

Sequence Length Distribution 60 0 0 60 0 0 60 0 0

Sequence Duplication Levels Help 0 0 60 0 0 60 0 0 60

Overrepresented sequences by sample Help 0 57 3 0 57 3 0 57 3

Top overrepresented sequences

FastQC: Top overrepresented sequences: Columns

Adapter Content Help 0 2 58 0 2 58 0 2 58

Status Checks Help

Cutadapt

Filtered Reads

Trimmed Sequence Lengths (3') Help

FastQC (trimmed)

Sequence Counts Help

Sequence Quality Histograms Help 60 0 0 60 0 0 60 0 0

Per Sequence Quality Scores Help 60 0 0 60 0 0 60 0 0

Per Base Sequence Content Help

Rollover for sample name

Per Sequence GC Content Help 5 34 21 5 34 21 5 34 21

Per Base N Content Help 60 0 0 60 0 0 60 0 0

Sequence Length Distribution 0 60 0 0 60 0 0 60 0

Sequence Duplication Levels Help 0 0 60 0 0 60 0 0 60

Overrepresented sequences by sample Help 0 57 3 0 57 3 0 57 3

Top overrepresented sequences

FastQC: Top overrepresented sequences: Columns

Adapter Content Help 60 0 0 60 0 0 60 0 0

Status Checks Help

DupRadar

Picard

Mark Duplicates Help

QualiMap

Genomic origin of reads Help

Gene Coverage Profile Help

RSeQC

Read Distribution

Inner Distance

Read Duplication

Junction Annotation

Junction Saturation

Infer experiment

Bam Stat

RSeQC: Bam Stat: Columns

Samtools

Percent mapped Help

Alignment stats

Samtools: stats: Alignment Stats: Columns

Flagstat

Samtools: flagstat: read count: Columns

Mapped reads per contig

STAR

Summary Statistics

STAR: Summary Statistics: Columns

Alignment Scores

Sample relationships

STAR_SALMON DESeq2 sample similarity

STAR_SALMON DESeq2 PCA plot

SortMeRNA

Software Versions

v1.25.1

Highlight Samples

Rename Samples

Show / Hide Samples

Sequence Counts

Sequence Quality Histograms

60

0

0

60

0

0

60

0

0

Per Sequence Quality Scores

60

0

0

60

0

0

60

0

0

Per Base Sequence Content

Per Sequence GC Content

7

36

17

7

36

17

7

36

17

Per Base N Content

60

0

0

60

0

0

60

0

0

Sequence Length Distribution

60

0

0

60

0

0

60

0

0

Sequence Duplication Levels

0

0

60

0

0

60

0

0

60

Overrepresented sequences by sample

0

57

3

0

57

3

0

57

3

Adapter Content

0

2

58

0

2

58

0

2

58

Status Checks

Trimmed Sequence Lengths (3')

Sequence Counts

Sequence Quality Histograms

60

0

0

60

0

0

60

0

0

Per Sequence Quality Scores

60

0

0

60

0

0

60

0

0

Per Base Sequence Content

Per Sequence GC Content

5

34

21

5

34

21

5

34

21

Per Base N Content

60

0

0

60

0

0

60

0

0

Sequence Length Distribution

0

60

0

0

60

0

0

60

0

Sequence Duplication Levels

0

0

60

0

0

60

0

0

60

Overrepresented sequences by sample

0

57

3

0

57

3

0

57

3

Adapter Content

60

0

0

60

0

0

60

0

0

Status Checks

Mark Duplicates

Genomic origin of reads

Gene Coverage Profile

Percent mapped